Method for evaluating probability of river blockage of landslide-debris flow geological disaster chain induced by rainfall after earthquake

Abstract

The invention discloses a method for evaluating the probability of river blockage of a landslide-debris flow geological disaster chain induced by rainfall after an earthquake, and the method comprises the following steps: 1), determining a research region for constructing a landslide occurrence probability evaluation model, and carrying out the meshing of the research region, and dividing the research region into a plurality of non-overlapping grid units; constructing a landslide occurrence probability evaluation model; acquiring a landslide susceptibility distribution map; 2) acquiring a joint probability distribution model of the occurrence frequency and the volume of the landslide in the to-be-detected area; 3) obtaining debris flow migration characteristics in the to-be-detected area; and 4) based on the rainfall intensity and the distribution data, through multiple Monte Carlo simulation, obtaining the river blocking probability of the river reach in the grid unit where the river channel is located in the to-be-detected area. According to the method, a complete evaluation process is provided, the river blocking probability of the river reach is scientifically quantified through comprehensive calculation of the landslide occurrence probability and the debris flow migration characteristics in combination with the Monte Carlo simulation technology, and important support is provided for disaster prevention and control.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster assessment, and specifically refers to a method for assessing the probability of river blockage in a landslide-debris flow geological disaster chain induced by post-earthquake rainfall. Background Art

[0002] The damage to the mountainous landform caused by an earthquake often results in a large area of loose accumulations, providing rich material sources for debris flows. Especially under the action of post-earthquake rainfall, these accumulations are extremely prone to debris flows, further triggering disasters such as river blockages and breach floods, posing a serious threat to downstream residents and infrastructure. However, current assessment methods for disaster chains mostly focus on the analysis of a single disaster type (such as landslides or debris flows). For example, the Chinese invention application with the application number CN202311853733.5 discloses a method and related products for predicting the risk of landslide-induced river blockage, realizing the risk prediction of landslide-induced river blockage. The Chinese invention application with the application number CN202410329944.7 discloses a method and system for determining debris flow-induced river blockage, both of which are for the determination of river blockage caused by a single disaster type, landslide or debris flow, and it is difficult to achieve a systematic assessment of multiple links such as regional landslide-debris flow-river blockage, and it is difficult to quantify the assessment of a disaster chain integrating multiple disasters. Summary of the Invention

[0003] To overcome the deficiencies of the above technologies, the purpose of the present invention is to provide a method for assessing the probability of river blockage in a landslide-debris flow geological disaster chain induced by post-earthquake rainfall, quantitatively analyze the probability of river blockage, and make up for the blank of regional assessment of multiple links such as regional landslide-debris flow-river blockage in the prior art.

[0004] To achieve the above purpose, the technical solution adopted by the present invention is as follows: A method for assessing the probability of river blockage in a landslide-debris flow geological disaster chain induced by post-earthquake rainfall, characterized by including the following steps: 1) Determine the research area for constructing the landslide occurrence probability assessment model, and divide the research area into a plurality of non-overlapping grid cells by grid; obtain the historical basic data of the research area and perform preprocessing, and construct a landslide occurrence probability assessment model based on the historical basic data; based on the relevant data of the area to be measured, evaluate the landslide occurrence probability values of each grid cell in the area to be measured through the landslide occurrence probability assessment model and display them in the corresponding grid cells of the area to be measured, so as to obtain a landslide susceptibility distribution map; the research area includes the area to be measured; both the historical basic data of the research area and the relevant data of the area to be measured include rainfall intensity and distribution data; 2) Obtain the historical landslide event data in the area to be measured and perform preprocessing; based on the historical landslide event data, obtain the probability density functions of the number of landslide occurrences and the landslide volume distribution, and obtain the joint probability distribution model of the number of landslide occurrences and the volume; 3) Obtain historical debris flow event data within the area to be measured; based on the historical debris flow event data, predict the debris flow migration characteristics; the debris flow migration characteristics include the debris flow migration path, the debris flow migration distance, and the intersection position between the debris flow and the river channel; 4) According to the landslide susceptibility distribution map, the joint probability distribution model of the landslide occurrence times and volume, the prediction result of the debris flow migration characteristics, and the river channel terrain data of the area to be measured, based on the rainfall intensity and distribution data, through multiple Monte Carlo simulations, obtain the probability of river blockage for the river reaches within the grid cells where the river channels are located in the area to be measured.

[0005] As a preferred solution, in step 1), the historical basic data of the research area includes historical data related to disaster-causing factors within the research area; the disaster-causing factors include inducing factors; the inducing factors include rainfall intensity and distribution; the preprocessing includes filling and correcting the outliers or missing values in the grid cells by using the interpolation method or the neighborhood averaging method.

[0006] Furthermore, earthquakes have occurred in the area to be measured in the past.

[0007] Furthermore, the rainfall intensity and distribution include one or more of the cumulative amount and spatio-temporal distribution characteristics of heavy rainfall, monthly rainfall, maximum monthly rainfall, and annual rainfall.

[0008] Even further, in the historical basic data, the cumulative amount of heavy rainfall is the cumulative amount and spatio-temporal distribution characteristics of the most recent or current heavy rainfall before the landslide occurred; the monthly rainfall is the monthly rainfall in the year when the landslide occurred; the maximum monthly rainfall and the annual rainfall are the maximum monthly rainfall and the annual rainfall in the year when the landslide occurred; Among the relevant data of the area to be measured, the cumulative amount of heavy rainfall uses the cumulative amount and spatio-temporal distribution characteristics of the most recent or current heavy rainfall before the evaluation time; the maximum monthly rainfall and the annual rainfall use the average values of the maximum monthly rainfall and the annual rainfall in the years after the earthquake; the monthly rainfall uses the monthly rainfall in the year of the evaluation time.

[0009] Furthermore, the disaster-causing factors further include one or more of topography and geomorphology, hydrological characteristics, geological structure, stratigraphic lithology, co-seismic landslides, post-seismic landslides, and the locations of landslides that occurred in the year before the landslide year to be evaluated.

[0010] As a preferred solution, the construction method of the landslide occurrence probability assessment model includes the following steps: construct a landslide historical event database within the research area and conduct model training.

[0011] Furthermore, the construction method of the landslide occurrence probability assessment model includes the following steps: Construct a database of historical landslide events in the study area: Based on the historical basic data of the study area, construct a database of historical landslide events, and randomly divide the data in the database of historical landslide events into a training set and a test set; the database of historical landslide events includes the location, time, scale of past landslides in the study area, and historical data related to disaster-causing factors at the locations where the landslides occurred; the scale is the landslide volume. Model training: Based on the historical basic data and the disaster-causing factors, train a machine learning model to obtain a trained model, that is, obtain a landslide occurrence probability evaluation model; in the training, the training set and test set of the database of historical landslide events are used as positive samples, and the relevant data of the locations where no landslides occurred in the historical basic data are used as negative samples. The landslide occurrence probability evaluation model outputs the landslide occurrence probability value of each grid cell by inputting the disaster-causing factor data of each grid cell, and the value range is from 0 to 1.

[0012] Furthermore, the construction method of the landslide occurrence probability evaluation model further includes screening key disaster-causing factors before model training; the key disaster-causing factors include rainfall intensity and distribution; the key disaster-causing factors also include the disaster-causing factors screened out from other disaster-causing factors except rainfall intensity and distribution; the disaster-causing factors used in the model training are the screened key disaster-causing factors, and the trained model outputs the landslide occurrence probability value of each grid cell by inputting the key disaster-causing factor data of each grid cell, and the value range is from 0 to 1.

[0013] Even further, the screening method of the key disaster-causing factors includes the random forest method or the correlation analysis method, and the recursive feature elimination technique is used to optimize factor selection; the correlation analysis method includes the Pearson correlation coefficient method or the information gain method.

[0014] Furthermore, the machine learning model includes the random forest algorithm.

[0015] Furthermore, the model training process further includes sampling the negative samples in combination with the sample sampling method.

[0016] As a preferred solution, the landslide susceptibility distribution map includes the landslide occurrence probability values of each grid cell in the area to be measured; the landslide occurrence probability values are obtained by inputting the relevant data of the grid cells in the area to be measured into the landslide occurrence probability evaluation model for analysis; the landslide occurrence probability values change according to the real-time data of rainfall intensity and distribution.

[0017] Furthermore, the relevant data of the area to be measured also includes the real-time data of other key disaster-causing factors.

[0018] As a preferred solution, the grid division process is repeated multiple times to divide the study area at different resolutions, obtaining multiple grids with different resolutions; the model training also includes performing optimal resolution analysis on the grids with different resolutions by combining multi-scale analysis; the landslide occurrence probability assessment model inputs the disaster-causing factor data of each grid unit in the area to be measured under the optimal resolution grid division, and outputs the landslide occurrence probability value of the corresponding grid unit.

[0019] As a preferred solution, in step 2), the historical landslide event data in the area to be measured includes the landslide occurrence time, location, and volume characteristics in the area to be measured; the preprocessing includes establishing a time series dataset of landslide occurrence times and a landslide volume dataset, filling in missing data, and removing abnormal data; the time series dataset of landslide occurrence times includes the number of landslides occurring within a set unit time arranged in chronological order during the historical process; the landslide volume dataset includes the distribution range of the landslide volume characteristics of each landslide event; the volume characteristic is the natural logarithm of the actual volume of the landslide.

[0020] As a preferred solution, in step 2), the probability assessment method for the number of landslides includes, based on the time series dataset of the number of landslides, using the Poisson distribution model to fit the number of landslides occurring within a unit time, and obtaining the probability and time series characteristics of the number of landslides through the fitted function; the probability density function of the number of landslides is as follows: ;

[0021] In the formula, P ( N ) is the probability of N landslides occurring within a unit time; λ is the average number of landslides occurring within a unit time, obtained through fitting; N is the specific number of landslides; The probability assessment method for the landslide volume distribution includes, based on the landslide volume dataset, using the power-law distribution model to fit the distribution of the landslide volume characteristics, and obtaining the probability of the landslide volume characteristics distribution through the fitted function; the probability density function of the landslide volume characteristics distribution is as follows: P ( V' ) = CV' -m In the formula, P ( V' ) is the probability that the landslide volume characteristic is V' ; m is the power-law exponent, controlling the steepness of the distribution, obtained through fitting; C is the normalization constant to ensure that the probability density integral is 1; V' is the volume characteristic, which is the natural logarithm of the actual volume of the landslide; The joint probability distribution model of the landslide frequency and volume is as follows: P ( N, V' ) = P ( N ) P ( V' ) In the formula, P ( N, V' ) is the joint probability of the landslide frequency and volume; P ( N ) is the probability of N landslides occurring within a unit time; P ( V' ) is the probability that the landslide volume feature is V' .

[0022] Furthermore, in the probability density function of the landslide occurrence frequency, the average number of landslides occurring within a unit time λ is obtained by fitting through the maximum likelihood estimation method.

[0023] Furthermore, in the probability density function of the landslide volume distribution, the power-law exponent m is obtained by linear fitting through the least squares method.

[0024] As a preferred solution, in step 3), the historical debris flow event data includes the source volume, transportation distance, movement path of the historical debris flow event, and the height difference between the landslide trigger point and the adjacent river; the source volume is the landslide volume in the corresponding historical landslide event.

[0025] As a preferred solution, in step 3), the prediction method for the debris flow transportation characteristics includes: Debris flow movement path prediction: Use DEM to calculate the slope of each grid cell in the area to be measured; Define the landslide trigger point in the area to be measured in the GIS environment and use it as the initial position of the debris flow; Based on the slope of each grid cell in the area to be measured, make the debris flow move along the steepest direction to obtain the movement path of the debris flow; Debris flow transportation distance calculation: Based on the historical debris flow event data, establish an empirical formula between the landslide volume, height difference, and transportation distance: ;

[0026] In the formula, L is the maximum transportation distance; V is the source volume; H is the height difference between the landslide trigger point and the adjacent river; a , b and cis a parameter obtained by fitting using historical debris flow event data.

[0027] Furthermore, the parameter a 、 b and c are obtained through regression analysis by the least squares method.

[0028] As a preferred solution, in step 4), the Monte Carlo simulation process includes the following steps: Sampling is carried out according to the landslide susceptibility distribution map of the area to be measured, and the grid cells in the grid are extracted as the locations where the landslide trigger points are located; According to the joint probability distribution model of the number of landslides and volume, the number of landslide events and the corresponding volume sizes are randomly assigned; Based on the grid cells where the landslide trigger points are located, the number of landslides and the volume, the migration path and migration distance of the debris flow are simulated; Based on the river channel terrain data, it is determined whether the migration path of the debris flow under each landslide event intersects with the river channel, and the grid cells where the intersections are located are determined; The landslide susceptibility distribution map is sampled multiple times and the migration path and migration distance of the debris flow are simulated; The number of times the debris flow intersects with the grid cells where the river channel is located during multiple simulation processes is counted N block , and the total number of simulations N total , and the probability of river blockage of the river section within a single grid cell is calculated P block : .

[0029] Furthermore, step 4) also includes: based on the real-time data of rainfall intensity and distribution, the landslide susceptibility distribution map is dynamically updated, so as to dynamically update the probability of river blockage of the river section within each grid cell.

[0030] Furthermore, the number of simulations in the Monte Carlo simulation process is at least 10,000 times.

[0031] Further, the method of sampling according to the landslide susceptibility distribution map of the area to be measured is as follows: After normalizing the landslide occurrence probability of the grid in the landslide susceptibility distribution map, the landslide occurrence probability of the grid unit after normalization is used as the sampling probability to sample the grid unit as the location where the landslide trigger point is located; According to the joint probability distribution model of the number of landslides and volume, the number of landslide events and the corresponding volume size are randomly assigned, including normalizing all the obtained joint probability data of the number of landslides and volume, and using the joint probability of the number of landslides and volume after normalization as the sampling probability to select the number of landslide events and the corresponding volume size. Setting the sampling probability for sampling makes the results more reasonable and scientific.

[0032] Further, during the Monte Carlo simulation process, random perturbations are added to the landslide occurrence location, occurrence frequency, volume distribution, and debris flow path.

[0033] As an optimal solution, step 4) further includes: statistically analyzing the river-blocking probability of the river sections within each grid unit to obtain the river-blocking probability interval; statistically analyzing the river width within the area to be measured to obtain the river width interval; dividing the river-blocking probability interval and the river width interval into multiple groups for grading; overlaying the grading results of the river-blocking probability and river width with the geographical information of the river section to generate the final river-blocking probability grade distribution map of the river section.

[0034] Further, the grading includes five levels, which are extremely high, high, medium, low, and extremely low in descending order of numerical value.

[0035] Even further, the grading adopts the natural breaks method.

[0036] The method of the present invention can combine real-time rainfall data to dynamically update the landslide susceptibility distribution and the simulation results of the debris flow path, ensuring the timeliness and accuracy of the river-blocking probability. Analyze the accumulation effect at the intersection of the debris flow and the upstream river section, considering the impact of upstream river-blocking events on the water flow and debris flow path of the downstream river section. Introduce the influence coefficient of the river width on the debris flow accumulation behavior. For example, a wider river section may reduce the river-blocking probability. Normalize the river width of the area to be measured to a value within the same interval as the evaluated river-blocking probability of the river, and divide these two sets of numerical values into 5 groups using the natural breaks method, and grade them as extremely high, high, medium, low, and extremely low in descending order of numerical value. Using the GIS platform, the river-blocking probability and the geographical information of the river section are overlaid using the matrix method to generate the final river-blocking probability grade distribution map of the river section, providing an intuitive reference for decision-makers.

[0037] The present invention also provides an evaluation system for the river-blocking probability of a post-earthquake rainfall-induced landslide-debris flow geological disaster chain, which is characterized in that it implements the above-mentioned evaluation method for the river-blocking probability of a post-earthquake rainfall-induced landslide-debris flow geological disaster chain, including: A grid unit division module for dividing the research area into grids; The landslide probability assessment model construction module is used to construct a landslide probability assessment model based on the historical basic data of the study area; A landslide susceptibility distribution map acquisition module is used to evaluate the landslide occurrence probability value of each grid unit in the test area through a landslide occurrence probability evaluation model based on relevant data of the test area and display it in the corresponding grid unit, thereby obtaining a landslide susceptibility distribution map; A joint probability distribution model acquisition module for the number of landslide occurrences and the volume is used to obtain a joint probability distribution model for the number of landslide occurrences and the volume based on historical landslide event data in the area to be tested; A debris flow migration characteristic acquisition module is used to predict debris flow migration characteristics based on historical debris flow event data in the area to be measured; The river blocking probability acquisition module is used to obtain the river blocking probability of each grid cell containing a river section in the test area through multiple Monte Carlo simulations based on the landslide susceptibility distribution map, the joint probability distribution model of landslide occurrence frequency and volume, the prediction results of debris flow migration characteristics, the river topography data of the test area, and the rainfall intensity and distribution data.

[0038] Furthermore, the landslide occurrence probability assessment model construction module is also used to determine the optimal gridding resolution; the grid units in the landslide susceptibility distribution map acquisition module and the river blocking probability acquisition module are grid units of the grid divided using the optimal resolution.

[0039] The present invention also provides a computer program product, including computer instructions, which is special in that: the computer instructions are used to enable a computer to execute the above-mentioned method for assessing the probability of river blocking by a landslide-mudslide geological disaster chain induced by rainfall after an earthquake.

[0040] Compared with the prior art, the present invention has the following beneficial effects: The present invention proposes a complete assessment process, which scientifically quantifies the probability of river blockage by comprehensively calculating the probability of landslide occurrence and debris flow migration characteristics and combining Monte Carlo simulation technology, thus providing important support for disaster prevention and control.

[0041] (1) Achieve a systematic assessment of the entire disaster chain The present invention realizes the full-process modeling and evaluation of the landslide-mudslide-river blocking disaster chain for the first time through multi-link quantitative analysis of landslide occurrence probability, landslide volume distribution, debris flow migration distance, and river blocking probability. Compared with the traditional single disaster assessment method, the present invention more comprehensively reflects the dynamic characteristics of the disaster chain and the causal relationship between its multiple links, providing reliable technical support for disaster risk analysis in complex geological environments.

[0042] (2) Improving the accuracy and applicability of the model The present invention introduces the random forest algorithm to construct a landslide susceptibility model, combines historical data to accurately identify the key control factors of landslides, and significantly improves the accuracy of landslide occurrence probability assessment. The power-law distribution model is used to describe the landslide volume probability distribution, which can capture the occurrence rules of extreme events (large-volume landslides) and enhance the robustness of the model. Based on the digital elevation model (DEM) and the steepest path algorithm, the debris flow migration path is simulated, and the spatial diffusion process after debris flow triggering is truly restored, which is especially suitable for complex terrain environments.

[0043] (3) Achieve an innovative breakthrough in the quantitative analysis of disaster chains Monte Carlo simulation technology is used to handle the uncertainties of each link of the disaster chain. By calculating the probability of river blockage in the river section through large-scale random sampling, the scientific nature and credibility of the evaluation results are significantly improved. The river blockage probability distribution map of the present invention clearly and intuitively shows the river blockage risks in different river sections, providing an important reference basis for regional disaster prevention and mitigation planning.

[0044] (4) Dynamically predict the evolution process of the disaster chain The present invention can adjust the simulation parameters of the landslide-debris flow migration path according to dynamic data such as real-time rainfall intensity and geological conditions, and update the river blockage probability evaluation results in real time. This dynamic response ability provides effective support for early disaster warning and real-time decision-making, and can help managers quickly identify potential risks and formulate corresponding emergency plans.

[0045] (5) Improve the management efficiency of the disaster chain The present invention takes into account both technical accuracy and computational efficiency in the method. By constructing step-by-step models and iterating the results of multiple links, the computational resource requirements are reduced, which is suitable for disaster assessment in large-scale regions. Utilizing the integration characteristics of the GIS platform, combining the landslide susceptibility distribution map, debris flow migration path, and river blockage probability distribution map output by the model with the regional infrastructure layout provides a visualization tool for the comprehensive management of the disaster chain and significantly improves the disaster management efficiency.

[0046] (6) Provide technical support for disaster reduction planning and emergency response The present invention can not only identify high-risk areas, but also quantify the risk levels of each link of the disaster chain, providing a scientific basis for disaster reduction planning. For example: Planning the location of reservoirs or dams: According to the river blockage probability distribution, optimize the siting of water conservancy facilities to reduce the risk of debris flow blocking the river channel. Optimize disaster prevention and protection measures: Such as through landslide treatment and debris flow interception projects, reduce the possibility of triggering the disaster chain. Assist in emergency response decision-making: During a disaster, a list of threatened areas can be quickly generated to help relevant departments accurately locate high-risk areas and optimize the scheduling and allocation of rescue resources.

[0047] (7) Strong technical applicability and high promotion value The technical methods adopted in the present invention (such as random forest, power law distribution fitting, DEM simulation and Monte Carlo technology) have high versatility and applicability, and can be flexibly adjusted according to the geological characteristics, rainfall conditions and disaster types of different regions.

[0048] (8) Make up for the shortcomings of existing technologies Traditional methods focus on the analysis of a single disaster (such as landslides or mudslides), and fail to reflect the coupling characteristics of the disaster chain. The present invention makes up for this shortcoming through comprehensive modeling. Existing river blocking probability assessments mostly use empirical methods, which make it difficult to quantify the uncertainty between multiple links. The present invention solves the problem of strong subjectivity of traditional empirical methods through numerical simulation and probability analysis, and the assessment results are more scientific. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A grid map generated by dividing the study area at a resolution of 150 meters in an embodiment of the present invention; Figure 2 This is an evolution diagram of key disaster-causing factors after an earthquake in an embodiment of the present invention; Figure 3 The probability distribution map of landslide occurrence in region X in the embodiment of the present invention; Figure 4 It is a probability distribution diagram of the landslide occurrence volume interval in the X area in the embodiment of the present invention; Figure 5 Schematic diagram of the steepest path of debris flow moving downward in an embodiment of the present invention; Figure 6 This is a distribution diagram of the probability level of river blockage in each river section in region X in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to better explain the present invention, the main contents of the present invention are further explained below in conjunction with the drawings and specific embodiments, but the contents of the present invention are not limited to the following embodiments.

[0051] The present invention provides a method for evaluating the probability of river blocking caused by a landslide-mudslide geological disaster chain induced by rainfall after an earthquake, comprising the following steps: 1) Landslide probability assessment 1.1) Determine the study area for constructing the landslide probability assessment model and grid the study area into multiple non-overlapping grid cells; obtain the historical basic data of the study area and perform preprocessing.

[0052] Historical basic data include the disaster-causing factors in the study area and their historical data. The disaster-causing factors include inducing factors, as well as one or more of topographic features, hydrological characteristics, geological structures, stratigraphic lithologies, coseismic landslides, post-seismic landslides, the location of landslides occurred in the year prior to the landslide year to be evaluated, etc. The historical data are obtained through remote sensing images, field investigations, and literature records.

[0053] The inducing factors are rainfall intensity and distribution, including one or more of the cumulative amount and spatio-temporal distribution characteristics of the most recent or current heavy rainfall before the landslide occurred, the monthly rainfall, the maximum monthly rainfall, and the annual rainfall in the year when the landslide occurred. The topographic features include one or more of the factors such as elevation, slope, aspect, curvature, topographic position index, topographic wetness index, land use, soil moisture, normalized difference vegetation index, vegetation coverage, etc. in the study area. The hydrological characteristics include one or more of the factors such as river network, river power index, fault distribution, etc. The stratigraphic lithology includes different lithologies, which are used to measure the physical and mechanical properties of the strata such as strength, porosity, etc.

[0054] Among them, the slope and aspect are extracted through the digital elevation model (DEM). The fault distribution includes the spatial distribution of the fault zone and its relative position to the slope surface. The river network includes the river distribution, and different river distributions have different weakening effects on slope stability. The vegetation coverage can characterize the influence of vegetation roots on soil reinforcement. The optional range of the study area is the global range or the area to be measured. If the global range is selected as the study area, the obtained landslide occurrence probability assessment model is universal. However, if the area to be measured is selected, the assessment result of the landslide occurrence probability assessment model for the area to be measured has higher accuracy.

[0055] Gridification: The study area is divided into multiple grid cells and standardized in a gridified form. Each grid cell represents a geological unit.

[0056] The number of grids is determined according to the size of the study area. If the study area is global, the study area is divided into at least 1000×1000 grids. If the study area is within a province, the study area is divided into at least 30×30 grids.

[0057] The study area is divided into multiple different grids with different resolutions through multiple gridifications, so as to determine the optimal resolution through multi-scale analysis in subsequent steps and improve the model prediction accuracy. Using the topographic elevation data, the study area is cut into multiple grids with different sizes, so as to select a suitable scale for analysis according to different requirements (such as accuracy, efficiency) in the future. For example: The study area is cut into three networks with different resolutions. The resolutions of the three networks are 10 meters, 30 meters, and 50 meters respectively. Through model training, the optimal resolution is selected as 30 meters, and then the grid with a resolution of 30 meters is selected for the subsequent evaluation process.

[0058] Preprocessing: Fill and correct outliers or missing values in grid cells using interpolation or neighborhood averaging methods.

[0059] 1.2) Based on the historical basic data of the study area, construct a landslide historical event database; and randomly divide the data in the landslide historical event database into a training set and a test set for model training and validation.

[0060] The landslide historical event database includes the location, time, scale of past landslides in the study area, and historical data related to hazard-causing factors at the locations where the landslides occurred.

[0061] 1.3) Obtain key hazard-causing factors.

[0062] The key hazard-causing factors include rainfall intensity and distribution. Rainfall intensity and distribution are the most critical hazard-causing factors for landslides and debris flows.

[0063] The key hazard-causing factors also include other hazard-causing factors obtained through screening. The screening process includes using the random forest method to screen among other hazard-causing factors except rainfall intensity and distribution; or, using correlation analysis to evaluate the correlation between other hazard-causing factors except rainfall intensity and distribution and the occurrence of landslides, screening out the hazard-causing factors that have the greatest impact on the occurrence of landslides, such as slope, lithology, etc., and using the recursive feature elimination (RFE) technique to optimize factor selection and avoid the problem of multicollinearity. The correlation analysis methods include the Pearson correlation coefficient method or the information gain method, screening out the first few factors with a large contribution difference from the following, and determining specifically according to the actual situation.

[0064] 1.4) Based on the historical basic data and key hazard-causing factors, construct a machine learning model - a landslide occurrence probability assessment model.

[0065] Adopt the Random Forest (RF) algorithm. Use the key hazard-causing factor data in the above-mentioned training set and test set as positive samples, and the key hazard-causing factor data at the locations where landslides did not occur in the historical basic data as negative samples. Combine sample sampling and multi-scale analysis of resolution to train the model, construct a classification model, and use the cross-validation method to optimize the model hyperparameters and improve the model accuracy. The trained model is the landslide occurrence probability assessment model. The model hyperparameters include the maximum depth of the decision tree, the number of split nodes, etc. The sampling method includes the Bootstrap sampling method to sample the negative samples. The multi-scale analysis performs iterative analysis on the resolution to obtain the optimal resolution.

[0066] For the landslide occurrence probability assessment model, the input parameter is the key hazard-causing factor of the grid cell at the optimal resolution, and the output parameter is the landslide occurrence probability value of the grid cell, with the value range from 0 to 1.

[0067] Random forests have strong non - linear modeling capabilities and can handle the relationship between complex landslide causative factors and the probability of landslide occurrence. The algorithm has good anti - overfitting ability and is suitable for high - dimensional data analysis.

[0068] Use the ROC curve to evaluate the classification performance of the model, and use the AUC (area under the curve) value to quantify the overall prediction ability of the model. Analyze the accuracy, recall rate, and F1 - score of landslide prediction using a confusion matrix to verify the reliability and applicability of the model results.

[0069] 1.5) Generate a landslide susceptibility distribution map based on the relevant data of the area to be measured.

[0070] Input the real - time data of the key causative factors in the grid cells of the area to be measured into the landslide occurrence probability assessment model trained in step 1.4), output the landslide occurrence probability value of each grid cell, and input the landslide occurrence probability value into the grid to draw the landslide susceptibility distribution map.

[0071] The relevant data includes the key causative factor data of the current state of the area to be measured. Among them, for the key causative factors input into the landslide occurrence probability assessment model, the rainfall intensity and distribution include the cumulative amount and spatio - temporal distribution characteristics of the most recent or current heavy rainfall before the evaluation time, the average maximum monthly rainfall and average annual rainfall in the years after an earthquake, and one or more of the monthly rainfall amounts in the year of the evaluation time.

[0072] In step 1), the landslide occurrence probability assessment process has the following advantages: (1) Application of the random forest algorithm Random forests construct multiple decision trees and vote to obtain the final classification result, overcoming the limitations of a single decision tree. The importance scoring function inside the algorithm can quantify the contributions of various control factors, providing a scientific basis for landslide mechanism analysis. Use the Bootstrap sampling method to handle the problem of uneven distribution of landslide event data to ensure the robustness of the model.

[0073] (2) Spatial resolution optimization Combine with the digital elevation model (DEM) to divide the study area into fine grids (such as 10 - meter or 30 - meter resolution) to ensure the spatial accuracy of the landslide susceptibility assessment results. Introduce multi - scale analysis in model training, evaluate the impact of different resolutions on prediction accuracy, and select the best resolution as the model input.

[0074] (3) Temporal dynamic analysis Introduce the dynamic data of rainfall events, and dynamically adjust the landslide occurrence probability value according to the cumulative effect of rainfall duration and intensity, improving the model's prediction ability for landslide triggering under extreme weather conditions.

[0075] (4)Model Validation and Performance Evaluation The classification performance of the model is evaluated using the ROC curve, where the AUC (area under the curve) value is used to quantify the overall prediction ability of the model. The confusion matrix is used to analyze the accuracy, recall, and F1 score of landslide prediction to verify the reliability and applicability of the model results.

[0076] 2) Evaluation of Landslide Occurrence Times and Volume The evaluation of landslide occurrence times and volume is one of the key steps of the present invention. The main purpose is to estimate the possible future landslide occurrence times and their volume distribution characteristics in the area to be measured based on the historical data of the area to be measured. Through the statistical analysis and probability distribution fitting of the historical landslide data in the area to be measured, accurate parameters can be provided, laying a foundation for the subsequent simulation of debris flow migration distance and calculation of river-blocking probability. The specific steps are as follows: 2.1) Data Collection and Preprocessing Obtain the records of historical landslide events in the area to be measured, mainly including parameters such as the time, location, volume, and scale of landslides, and establish a time series dataset of landslide occurrence times and a landslide volume dataset.

[0077] Among them, the time series dataset of landslide occurrence times includes the number of landslides within a set unit time arranged in chronological order during the historical process. The set unit time is annual or quarterly. The landslide volume dataset includes the distribution range of landslide volume characteristics in all landslide events, as well as the maximum value, minimum value, and mean value of all landslide volume characteristics in the dataset, constructing a landslide volume characteristic dataset. Among them, the landslide volume characteristic V' is the natural logarithm of the original landslide volume V to reduce the influence of extreme values and make the data more conform to statistical laws. The data sources include remote sensing images, field surveys, monitoring system records, and existing geological disaster databases.

[0078] Preprocessing: Speculate and complete the missing data using the landslide characteristics of adjacent areas to ensure the integrity of the data. Abnormal values in the dataset are removed using statistical methods such as the interquartile range method.

[0079] 2.2) Probability Evaluation of Landslide Occurrence Times Use the Poisson distribution model to fit the number of landslides occurring within a unit time, and predict the number of landslides through the fitted function. The Poisson distribution is widely applicable to describe the number of random events occurring within a unit time, and its probability density function is: (Equation 1) P ( N ) is the probability of occurring N times of landslides within a unit time; λ is the average number of landslides occurring within a unit time (Poisson intensity);N is the specific number of landslides. Calculate the parameters by the maximum likelihood estimation method (MLE) λ , that is, the average incidence rate of landslides.

[0080] Fit the frequency of landslide events per unit time to determine the temporal characteristics of landslide occurrence. Use the actual number of landslide occurrences data to verify the fitting effect of the Poisson distribution model, and use the chi-square test to evaluate the significance of the model. If there is an obvious seasonal variation in the landslide occurrence time, a sine function can be further introduced to correct the Poisson distribution to make it more consistent with the periodic variation of rainfall. The formula is as follows: λ = λ 0(1 + lsin ( wt + φ )) (Equation 2) In the formula, λ is the average number of landslides per unit time (Poisson intensity); λ 0 is the average incidence rate of the Poisson distribution without seasonal influence; t is the time variable, which can be expressed in days, months or years; w is the frequency of the sine function, which controls the speed of periodic change, such as the relationship with the rainfall cycle; φ is the phase of the sine function, which determines the starting point of seasonal change; l is the amplitude of the sine function, which represents the intensity of seasonal influence.

[0081] 2.3) Probability assessment of landslide volume distribution Based on the above landslide volume dataset, use the power-law distribution model to fit the landslide volume characteristic distribution. Research shows that landslide events in nature mostly follow the power-law distribution, and its probability density function is: P ( V' ) = CV' -m (Equation 3) P ( V' ) is the probability that the landslide volume characteristic is V' , V' = lnV , V is the landslide volume; m is the power-law exponent, which controls the steepness of the distribution; C is the normalization constant to ensure that the probability density integral is 1. After taking the logarithm of the landslide volume data, use the least squares method for linear fitting to obtain the parameters m .

[0082] 2.4) Joint distribution assessment of landslide number and volume Under the assumption that the number of landslides and the landslide volume are independent, the joint probability is the product of the probabilities of the two: P ( N, V' ) = P ( N ) P ( V' ) (Equation 4) According to the joint probability distribution model, the number and volume of future landslides are simulated through random sampling to provide input parameters for the subsequent debris flow travel distance simulation. The Monte Carlo method is used to generate multiple sets of sample data to form possible combinations of the number and volume of landslides.

[0083] Technological innovation and details of this step: (1) Combination of seasonality and randomness The traditional Poisson distribution assumes that the intensity of event occurrence is constant, but seasonal factors such as rainfall have a significant impact on the occurrence frequency of landslides. By introducing the modified Poisson distribution, the seasonal fluctuations of landslide events are incorporated into the model in this invention, significantly improving the timeliness and accuracy of prediction.

[0084] (2) Joint distribution modeling The joint distribution analysis of the number and volume of landslides provides rich input data for landslide-debris flow risk assessment, and can capture the statistical relationship between the two, laying a foundation for the physical authenticity of the model.

[0085] 3) Debris flow travel distance simulation The debris flow travel distance simulation is one of the core technical links of this invention. Its purpose is to accurately predict the travel path and influence range of debris flow after triggering by analyzing the volume of landslide source materials, terrain elevation difference, and historical debris flow movement characteristics. This link provides key spatial parameter support for the assessment of the probability of river blockage. The specific steps are as follows: 3.1) Data preparation and feature extraction Through on-site investigation, literature records, and remote sensing images, etc., collect the source material volume, travel distance, movement path of historical debris flow events in the area to be measured, and the elevation difference between the landslide trigger point and the adjacent river.

[0086] Travel distance: Use GIS technology to obtain high-resolution digital elevation data (such as 10-meter or 30-meter resolution) of the area to be measured to provide a terrain basis for the travel path simulation. This resolution is the best resolution in step 1).

[0087] Movement path: Extract the location and elevation information of the river network distribution and the vegetation coverage data.

[0088] Source material volume: The collected landslide event data provides the material quantity of the source, which can be directly used as the input parameter for debris flow simulation.

[0089] Height difference: Calculate the elevation difference between the landslide trigger point and the target area using DEM; the target area refers to the river where the debris flow reaches.

[0090] 3.2) Determination of debris flow migration path The debris flow migration path is driven by gravity, and its path selection usually moves along the steepest slope direction. The path simulation method based on the steepest path algorithm includes: calculating the slope of each grid using DEM to simulate the initial flow direction of the debris flow; determining the landslide trigger point in the GIS environment and taking it as the initial position of the debris flow; generating slope data according to DEM; iteratively updating the migration path of the debris flow along the steepest slope direction until the path crosses the river channel or reaches a flat area.

[0091] 3.3) Calculation of debris flow migration distance Based on the historical debris flow event data, the empirical formula between the landslide volume, height difference and migration distance is established as follows: (Equation 5) L is the maximum migration distance, that is, the farthest distance of debris flow migration; V is the source volume, that is, the landslide volume in step 2), H is the elevation difference between the landslide trigger point and the target area; a 、 b and c are parameters. Use the least squares method to perform regression analysis on historical data to fit the best parameters of the empirical formula.

[0092] 4) Blocking river probability assessment: The blocking river probability assessment is the final link of the present invention. By quantitatively analyzing the possibility of the debris flow movement path crossing the river channel, it provides a scientific basis for disaster risk area division and emergency response. The core of the blocking river probability assessment is based on the previous landslide and debris flow simulation results, using the Monte Carlo method to simulate multiple landslide-debris flow events, and counting the occurrence frequency of river blocking in the river section, so as to calculate the probability distribution of river blocking in the river section. The specific steps are as follows: 4.1) Input data preparation Landslide susceptibility distribution map: Generated by the landslide occurrence probability assessment, marking the probability distribution of landslide events in the area and providing the probability basis for the landslide trigger point.

[0093] Joint probability distribution model of landslide occurrence times and volume: Characterize the landslide occurrence times and the corresponding volume probability, and determine the source characteristics of different landslide events.

[0094] Debris flow migration path simulation results: Include the debris flow movement path, migration distance and crossing position with the river channel for each simulation.

[0095] River channel terrain data: Generate river channel networks and their profile features based on high-resolution digital elevation models (DEM) to describe the width and height of river reaches.

[0096] 4.2) Monte Carlo simulation process Based on the landslide susceptibility distribution map, determine the grid cell division resolution of the area to be measured. Sample based on the landslide occurrence probability of each grid cell to select the location of the landslide trigger point. Among them, the sampling probability of each grid cell, that is, the probability that each grid cell is selected as the landslide trigger point, is the probability obtained by normalizing the landslide occurrence probability within the grid cell. The specific acquisition method is as follows: Normalize the landslide occurrence probability values within the grid; the value obtained by normalizing each grid cell is the sampling probability of each grid cell.

[0097] According to the joint probability distribution model of landslide occurrence times and volume, randomly assign the number of landslide events and the corresponding volume size according to the joint probability of landslide occurrence times and volume (after normalizing the data of landslide occurrence times and volume obtained from the joint probability distribution model of landslide occurrence times and volume, use the normalized data as the sampling probability for sampling).

[0098] Use the debris flow migration model to calculate the debris flow migration distance of each landslide event. The migration path starts from the grid cell where the landslide trigger point is located and moves along the steepest slope direction around the grid cell, while considering the influence of landslide volume, elevation difference, and frictional force along the way, and dynamically adjusts the flow range of the debris flow.

[0099] Combine the simulated debris flow path with the river channel terrain data to determine whether the debris flow path intersects with the river channel and determine the grid cell where the intersection occurs.

[0100] Count the number of times the debris flow intersects with the grid cells where the river channel is located, that is, the number of river blocking events N block in the river reach within the grid cell, as well as the total number of simulations N total . Calculate the river blocking probability P block of the river reach within the grid cell: (Equation 6) P block is the river blocking probability of the river reach within a single grid cell; N block is the number of river blocking events in the river reach within a single grid cell; N total is the total number of Monte Carlo simulations, preferably not less than 10,000 times.

[0101] 4.3) Model optimization and dynamic adjustment Add random perturbations to the landslide occurrence locations, occurrence frequencies, volume distributions, and debris flow paths. Combine real-time rainfall data to dynamically update the landslide susceptibility distribution and debris flow path simulation results, ensuring the timeliness and accuracy of the river-blocking probability. Analyze the accumulation effect at the intersection of the debris flow and the upstream river section, considering the impact of upstream river-blocking events on the water flow and debris flow paths in the downstream section. Introduce the influence coefficient of river width on debris flow accumulation behavior. For example, a wider river section may reduce the river-blocking probability, while a steeper river section may accelerate the erosion of the accumulated materials. Use the GIS platform to overlay and display the river-blocking probability with the river section geographical information to generate a risk distribution map, providing an intuitive reference for decision-makers.

[0102] The specific operation method is as follows: Statistically analyze the river-blocking probability of each grid cell section to obtain the river-blocking probability interval; Statistically analyze the river width within the area to be measured to obtain the river width interval; Use the natural break method to divide the river-blocking probability interval and the river width interval into five groups for grading, which are extremely high, high, medium, low, and extremely low in order of numerical value; Overlay the grading results of the river-blocking probability and the river width with the river section geographical information to generate the final river-blocking probability grade distribution map in the GIS platform; Based on the real-time data of rainfall intensity and distribution, dynamically update the landslide susceptibility distribution map, thereby dynamically updating the river-blocking probability of each grid cell section and the final river-blocking probability grade distribution map.

[0103] Technical innovations and details of this step: (1) Multi-source data fusion Fuse the landslide probability distribution, landslide volume model, debris flow path simulation, and river channel topographic features to construct a multi-dimensional probability analysis framework, effectively improving the accuracy of river-blocking probability assessment.

[0104] (2) Dynamically adjusted Monte Carlo simulation Ensure the statistical stability and credibility of the model results through large-scale sampling (such as more than 10,000 simulations).

[0105] (3) Refined analysis of the river-blocking mechanism Consider the multiple impacts of the movement characteristics of debris flows and river section characteristics (such as width, slope, vegetation coverage rate) on river-blocking behavior, improving the rough assumptions of traditional empirical methods.

[0106] The above method can be implemented through the GIS platform.

[0107] The present invention also provides a system for assessing the river-blocking probability of post-earthquake rainfall-induced landslide-debris flow geological disaster chains, which implements the above method for assessing the river-blocking probability of post-earthquake rainfall-induced landslide-debris flow geological disaster chains, including: Grid cell division module, used to divide the study area into grids; Landslide occurrence probability assessment model construction module, used to determine the optimal grid resolution based on the historical basic data of the study area and construct a landslide occurrence probability assessment model; Landslide susceptibility distribution map acquisition module, used to evaluate the landslide occurrence probability values of each grid cell at the optimal resolution of the area to be measured based on the relevant data of the area to be measured through the landslide occurrence probability assessment model and display them in the corresponding grid cells, so as to obtain the landslide susceptibility distribution map; Joint probability distribution model acquisition module of landslide occurrence times and volume, used to obtain the joint probability distribution model of landslide occurrence times and volume based on the historical landslide event data of the area to be measured; Debris flow migration characteristic acquisition module, used to predict the debris flow migration characteristics based on the historical debris flow event data of the area to be measured; River blocking probability acquisition module, used to obtain the river blocking probabilities of each grid cell containing river reaches at the optimal resolution in the area to be measured through multiple Monte Carlo simulations based on the landslide susceptibility distribution map, the joint probability distribution model of landslide occurrence times and volume, the predicted results of debris flow migration characteristics, the river channel terrain data of the area to be measured, and the rainfall intensity and distribution data.

[0108] Taking the X area after an earthquake as an example below, the river blocking probability of the landslide-debris flow geological disaster chain induced by post-earthquake rainfall is evaluated. The 15th anniversary day after the earthquake in the X area is selected as the prediction time to verify the method of the present invention, that is, the data of the area to be measured at the prediction time is the relevant data (real-time data) of the area to be measured in the method of the present invention.

[0109] 1) Select the area to be measured, that is, the X area, as the study area, and divide the X area into multiple grid cells by grid; obtain the historical basic data of each grid cell in the X area, and perform data filling and correction. The grid division is carried out at different resolutions, including 30 meters, 50 meters, 80 meters, 100 meters, and 150 meters, and the X area is divided into multiple grids with different resolutions, such as Figure 1 shown.

[0110] In this embodiment, the random forest method is used to analyze the important landslide-related characteristics of the X area. The disaster-causing factors included in the analysis are elevation, slope, aspect, curvature, topographic position index, stratum lithology, topographic wetness index, river network, river power index, fault distribution, location of co-seismic landslides, location of landslides occurred in the previous year, maximum monthly rainfall, annual rainfall, land use, soil moisture, and normalized vegetation index. The analysis results are as Figure 2As shown, in the years after the earthquake, the importance of elevation, slope aspect, normalized difference vegetation index, the locations of co-seismic landslides, and the locations of landslides in the year prior to the year of the evaluated landslides dominates. With the evolution of time, by the prediction time, soil moisture, rainfall intensity, and distribution dominate, that is, the key disaster-causing factors only remain soil moisture, rainfall intensity, and distribution.

[0111] In this embodiment, all disaster-causing factors including elevation, slope, slope aspect, curvature, topographic position index, formation lithology, topographic wetness index, river network, river power index, fault distribution, the locations of co-seismic landslides, the locations of landslides in the previous year, maximum monthly rainfall, annual rainfall, land use, soil moisture, and normalized difference vegetation index are used to construct a landslide historical event database and a landslide occurrence probability evaluation model. The random forest algorithm is adopted, and the sampling method includes the Bootstrap sampling method. Through the multi-scale analysis of the resolution, it is determined that the resolution of 30 meters is the optimal resolution, which can better obtain the ground topographic feature information of Area X. The cross-validation method is used to optimize the model hyperparameters. After optimization, the maximum depth and the number of splitting nodes of the decision tree are 24. The AUC of the trained model is 0.92, meeting the requirements for the training of the classification model. For the landslide occurrence probability evaluation model, all the disaster-causing factor data at the optimal resolution are input, and the landslide occurrence probability value of each grid cell is output, with the value range from 0 to 1.

[0112] Generating a landslide susceptibility distribution map: Obtain all the disaster-causing factor data of each grid cell in Area X at the prediction time, input the above-mentioned landslide occurrence probability evaluation model, the model outputs the landslide occurrence probability value, and present the landslide occurrence probability value of this grid in each grid to obtain the landslide susceptibility distribution map as Figure 3 shown. In the figure, the area within the red dashed line represents Area X.

[0113] 2) Evaluation of the number and volume of landslides Obtain the records of historical landslide events in Area X, mainly including parameters such as the time, location, volume, and scale of landslides, and establish a time series dataset of the number of landslides and a dataset of landslide volumes.

[0114] Using the Poisson distribution model, solve the probability of the number of landslides based on the time series dataset of the number of landslides, and the parameter λ is 2. Then the probabilities of 1, 2, 3, 4, and 5 landslides are: 0.27, 0.27, 0.18, 0.09, and 0.03 respectively. Using the power-law distribution model, fit the landslide volume distribution based on the landslide volume dataset, and the probability distributions of each volume interval after fitting are as Figure 4 shown. Then the joint probability distribution model function of the number and volume of landslides is the multiplication of the probabilities of different numbers and the probabilities of different volume intervals.

[0115] 3) Debris flow migration distance simulation Debris flow migration path: Calculate the slope of each grid using DEM. The debris flow migration path starts from the selected grid and flows towards the grid with the lowest elevation among the eight surrounding grids, as Figure 5 shown.

[0116] Debris flow migration distance: Based on historical debris flow event data, establish an empirical formula between the source volume, elevation difference, and migration distance. Through fitting with historical event data, the relevant parameters a, b, and c are obtained as 0.28, 0.22, and 0.93 respectively. Then the calculation formula for the debris flow migration distance is:[[]]

[0117] 4) Blockage probability assessment Input the landslide susceptibility distribution map, and randomly select landslide trigger points based on the landslide occurrence probability of each grid cell; Input the joint probability distribution model of the number and volume of landslides, and randomly assign landslide volumes based on the grid cell where the landslide starting point is located; Input the simulation results of the debris flow migration path, and obtain the debris flow movement path and migration distance based on the landslide volume and the elevation of each grid cell; Input the river channel terrain data, describe the width, height, and basin characteristics of the river section, and determine whether the debris flow migration path intersects with the river channel.

[0118] Through 10,000 Monte Carlo simulations of landslide-debris flow events, count the number of times the debris flow intersects with the river section in each grid cell, and calculate the blockage probability of the river section in each grid cell.

[0119] Divide the two sets of numerical values of the river width and the river blockage probability in area X into five groups using the natural breaks method, and classify them into extremely high, high, medium, low, and extremely low levels according to the numerical values. Using the GIS platform, overlay the blockage probability with the river section geographical information using the matrix method to generate the final river section blockage probability grade distribution map, providing an intuitive reference for decision-makers, as Figure 6 shown. The extremely high, high, medium, low, and extremely low levels are shown as red, orange, yellow, light green, and dark green respectively.

[0120] The prediction results show that the blockage probability of the river section intersecting with the debris flow gully mouth is the highest. By comparing the blockage probability and the actual blockage events in the 15 years after the earthquake, more than 90% of the events occurred in the high blockage probability grade interval, proving the accuracy of the prediction results. And there is still a 21% higher blockage risk for the river channels in area X in the future.

[0121] Other parts not described belong to the prior art.

Claims

1. A method for assessing the probability of river blocking caused by landslide-mudslide geological disaster chain induced by rainfall after an earthquake, characterized in that: The following steps are involved: 1) Determine a study area for constructing a landslide probability assessment model, and grid the study area into a plurality of non-overlapping grid units; obtain historical basic data of the study area, and construct a landslide probability assessment model; based on relevant data of the area to be tested, evaluate the landslide probability values ​​of each grid unit in the area to be tested through the landslide probability assessment model and display them in the corresponding grid units in the area to be tested, thereby obtaining a landslide susceptibility distribution map; the study area includes the area to be tested; the historical basic data of the study area and the relevant data of the area to be tested both include rainfall intensity and distribution data; 2) Obtaining historical landslide event data in the area to be tested; based on the historical landslide event data, obtaining the probability density function of the number of landslide occurrences and the landslide volume distribution, and obtaining a joint probability distribution model of the number of landslide occurrences and the volume; 3) Obtaining historical debris flow event data in the area to be measured; based on the historical debris flow event data, predicting debris flow migration characteristics; the debris flow migration characteristics include debris flow migration path, debris flow migration distance, and intersection position of debris flow and river channel; 4) Based on the landslide susceptibility distribution map, the joint probability distribution model of landslide occurrence frequency and volume, the prediction results of debris flow migration characteristics, and the river topography data of the test area, based on rainfall intensity and distribution data, through multiple Monte Carlo simulations, the probability of river blockage in the river section within the grid unit where the river channel in the test area is located is obtained.

2. The method for evaluating the probability of river blocking caused by post-earthquake rainfall-induced landslide-mudslide geological disaster chain according to claim 1 is characterized in that: In step 1), the historical basic data of the study area includes historical data related to disaster factors in the study area; the disaster factors include inducing factors; the inducing factors include rainfall intensity and distribution.

3. The method for evaluating the probability of river blocking caused by post-earthquake rainfall-induced landslide-mudslide geological disaster chain according to claim 2 is characterized in that: The method for constructing the landslide occurrence probability assessment model comprises the following steps: Construct a landslide historical event database in the study area: Based on the historical basic data of the study area, construct a landslide historical event database, and randomly divide the data in the landslide historical event database into a training set and a test set; the landslide historical event database includes the location, time, scale and historical data related to the hazard factors of the landslides in the study area in the past; Model training: Based on the historical basic data and disaster-causing factors, the machine learning model is trained to obtain a trained model, that is, a landslide occurrence probability assessment model; in the training, the training set and the test set of the landslide historical event database are used as positive samples, and the relevant data of the locations where landslides did not occur in the historical basic data are used as negative samples; The landslide occurrence probability assessment model inputs the disaster factor data of each grid unit and outputs the landslide occurrence probability value of the grid unit, with a value range of 0 to 1.

4. The method for evaluating the probability of river blocking caused by post-earthquake rainfall-induced landslide-mudslide geological disaster chain according to claim 3 is characterized in that: The method for constructing the landslide probability assessment model also includes screening key disaster factors before model training; the key disaster factors include rainfall intensity and distribution; the key disaster factors also include disaster factors screened out from other disaster factors except rainfall intensity and distribution; the disaster factors used in the model training are the screened key disaster factors, and the trained model outputs the landslide probability value of each grid unit by inputting the key disaster factor data of the grid unit, and the value range is 0 to 1.

5. The method for evaluating the probability of river blocking caused by post-earthquake rainfall-induced landslide-mudslide geological disaster chain according to claim 3 is characterized in that: The gridding process is repeated multiple times to divide the study area into different resolutions to obtain multiple grids with different resolutions; the model training also includes performing optimal resolution analysis on grids with different resolutions in combination with multi-scale analysis; the landslide occurrence probability assessment model inputs the disaster factor data of each grid unit in the test area under the optimal resolution grid division, and outputs the landslide occurrence probability value of the corresponding grid unit.

6. The method for evaluating the probability of river blocking caused by landslide-mudslide geological disaster chain induced by rainfall after an earthquake according to claim 1 is characterized in that: In step 2), the probability assessment method of the number of landslide occurrences includes fitting the number of landslide occurrences per unit time using a Poisson distribution model, and obtaining the probability and time series characteristics of the number of landslide occurrences through the fitted function; The probability density function of the number of landslide occurrences is as follows: ； In the formula, P ( N ) is the time unit N The probability of a landslide; λ is the average number of landslides per unit time, obtained by fitting; N is the specific number of landslide occurrences; The probability evaluation method of the landslide volume distribution includes fitting the landslide volume characteristic distribution by using a power law distribution model, and obtaining the probability of the landslide volume characteristic distribution by the fitted function; the probability density function of the landslide volume characteristic distribution is as follows: P （ V' ） =CV' -m ； In the formula, P ( V' ) is the volume characteristic of the landslide V' probability; m is the power law exponent, which controls the steepness of the distribution and is obtained by fitting; C is the normalization constant, which ensures that the probability density integral is 1; V' is the natural logarithm of the actual volume of the landslide; The joint probability distribution model of landslide number and volume is as follows: P （ N, V' ）= P （ N ） P （ V' ）； In the formula, P ( N, V' ) is the joint probability of landslide number and volume; P ( N ) is the time unit N The probability of a landslide; P ( V' ) is the volume characteristic of the landslide V' probability.

7. The method for evaluating the probability of river blocking caused by landslide-mudslide geological disaster chain induced by rainfall after an earthquake according to claim 1 is characterized in that: In step 3), the method for predicting debris flow migration characteristics includes: Prediction of debris flow migration path: Use DEM to calculate the slope of each grid unit in the test area; define the landslide trigger point in the test area in the GIS environment, and use the landslide trigger point as the initial position of the debris flow; based on the slope of each grid unit in the test area, make the debris flow move in the steepest direction, so as to obtain the movement path of the debris flow; Calculation of debris flow migration distance: Based on historical debris flow event data, an empirical formula between landslide volume, height difference and migration distance is established: ； In the formula, L is the maximum migration distance; V is the source volume; H is the height difference between the landslide triggering point and the adjacent river; a , b and c are parameters obtained by fitting using historical debris flow event data.

8. The method for evaluating the probability of river blocking caused by landslide-mudslide geological disaster chain induced by rainfall after an earthquake according to claim 1 is characterized in that: Step 4), the Monte Carlo simulation process includes the following steps: Sampling is performed according to the landslide susceptibility distribution map of the area to be tested, and the grid cells in the grid are extracted as the locations of the landslide triggering points; According to the joint probability distribution model of landslide occurrence frequency and volume, the number of landslide events and the corresponding volume size are randomly assigned; Based on the grid cells where the landslide trigger point is located, the number and volume of landslides, the migration path and distance of debris flow are simulated; Based on the river topography data, determine whether the migration path of the debris flow under each landslide event intersects the river channel, and determine the grid unit where the intersection is located; Sampling the landslide susceptibility distribution map multiple times and simulating the debris flow migration path and migration distance; Count the number of times the debris flow crosses each grid unit where the river is located during multiple simulations N block , and the total number of simulations N total , calculate the probability of river blockage in a single grid cell P block : 。 9. The method for assessing the probability of river blocking caused by landslide-mudslide geological disaster chain induced by rainfall after an earthquake according to any one of claims 1 to 8, characterized in that: The step 4) also includes: counting the probability of river blocking in the river section within each grid unit to obtain the river blocking probability interval; counting the river width in the test area to obtain the river width interval; dividing the river blocking probability interval and the river width interval into multiple groups for classification; superimposing the river blocking probability and river width classification results with the river section geographic information to generate a final river section river blocking probability level distribution map.

10. A system for assessing the probability of river blocking caused by landslide-mudslide geological disaster chain induced by rainfall after an earthquake, characterized in that: The method for evaluating the probability of river blocking caused by landslide-mudslide geological disaster chain induced by rainfall after an earthquake as described in any one of claims 1 to 9 comprises: Grid unit division module, used to divide the study area into grids; The landslide probability assessment model construction module is used to construct a landslide probability assessment model based on the historical basic data of the study area; A landslide susceptibility distribution map acquisition module is used to evaluate the landslide occurrence probability value of each grid unit in the test area through a landslide occurrence probability evaluation model based on relevant data of the test area and display it in the corresponding grid unit, thereby obtaining a landslide susceptibility distribution map; A joint probability distribution model acquisition module for the number of landslide occurrences and the volume is used to obtain a joint probability distribution model for the number of landslide occurrences and the volume based on historical landslide event data in the area to be tested; A debris flow migration characteristic acquisition module is used to predict debris flow migration characteristics based on historical debris flow event data in the area to be measured; The river blocking probability acquisition module is used to obtain the river blocking probability of each grid cell containing a river section in the test area through multiple Monte Carlo simulations based on the landslide susceptibility distribution map, the joint probability distribution model of landslide occurrence frequency and volume, the prediction results of debris flow migration characteristics, the river topography data of the test area, and the rainfall intensity and distribution data.

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