Regional landslide prediction method and device based on transient rainfall infiltration

Abstract

The invention relates to the technical field of landslide prediction, and discloses a regional landslide prediction method and device based on transient rainfall infiltration, and the method comprises the steps: constructing a comprehensive database of rainfall type group-occurring landslides, and the comprehensive database comprises a plurality of background data; performing sensitivity analysis on the background data by using a preset background parameter analysis model to obtain a sensitivity analysis result; performing preset rainfall parameter analysis on the to-be-studied area to obtain a rainfall parameter threshold value of the to-be-studied area, and constructing a regional landslide threshold value curve according to the rainfall parameter threshold value of the to-be-studied area; and predicting the landslide instability coefficient of the target area according to the sensitivity analysis result and the regional landslide threshold curve. According to the method, the adjustable regional rainfall threshold curve is constructed by analyzing the sensitivity of multiple background data and quantifying the influence of key parameters on the landslide instability coefficient, uncertainty analysis is added during model verification, and the landslide prediction stability and accuracy of the model under different geological and rainfall conditions are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of landslide prediction, and particularly relates to a regional landslide prediction method and device based on transient rainfall infiltration. Background Art

[0002] Currently, landslide prediction techniques usually rely on empirical rainfall threshold models, which are mainly based on the relationship between historical rainfall events and landslide occurrences, and the parameters of concern are mainly rainfall intensity and duration. However, these empirical threshold models lack a physical basis and usually cannot accurately reflect complex slope instability mechanisms, especially with low prediction accuracy when rainfall conditions, geological characteristics, or hydrological conditions change. The existing technologies have the following main deficiencies:

[0003] (1) Lack of physical basis: The empirical models fail to explain the actual physical relationship between rainfall and landslide instability. Since the models do not consider hydro-geotechnical dynamic processes, especially the process of rainfall infiltration affecting the groundwater level, especially the adaptability to special geological types is weak.

[0004] (2) Failure to consider the influence of transient rainfall infiltration: Empirical models usually ignore the time variation of rainfall infiltration, and the instantaneous infiltration of rainfall will significantly affect the hydraulic state of the slope body, thereby affecting the instability coefficient.

[0005] (3) Failure to consider parameter uncertainty: Existing models usually assume fixed parameter values, ignoring the uncertainty of soil parameters, hydrological conditions, and rainfall characteristics, resulting in high-risk prediction results.

[0006] (4) Insufficient regional differences: When existing rainfall threshold models are applied to different geological regions, it is difficult to reflect the regional differences in geological conditions and rainfall characteristics. Existing models often cannot be optimized for these characteristics. Secondly, empirical models are difficult to explain the actual physical relationship between rainfall and landslide instability, so it is difficult to be universal in different geological environments. Summary of the Invention

[0007] In view of this, the present invention provides a regional landslide prediction method and device based on transient rainfall infiltration to solve the problems of low prediction accuracy and poor applicability of rainfall-induced landslides.

[0008] In a first aspect, the present invention provides a regional landslide prediction method based on transient rainfall infiltration, and the method includes:

[0009] Construct a comprehensive database of rainfall-induced mass landslides, where the comprehensive database includes multiple background data;

[0010] Use a preset background parameter analysis model to perform sensitivity analysis on the background data to obtain the sensitivity analysis results of each background data;

[0011] Perform a preset rainfall parameter analysis on the study area to obtain the rainfall parameter thresholds of the study area, and construct a regional landslide threshold curve based on the rainfall parameter thresholds of the study area;

[0012] Predict the landslide instability coefficient of the target area according to the sensitivity analysis results and the regional landslide threshold curve. The landslide instability coefficient is used to characterize the likelihood of a landslide occurring in the target area.

[0013] The regional landslide prediction method based on transient rainfall infiltration provided by the present invention determines the key parameters affecting landslides through sensitivity analysis of multiple background data, improves the robustness and adaptability of the preset background parameter analysis model by quantifying the influence of key parameters on the landslide instability coefficient, constructs an adjustable regional rainfall threshold curve through rainfall parameter analysis, and adds uncertainty analysis during model verification to ensure the landslide prediction stability and accuracy of the model under different geological and rainfall conditions.

[0014] In an alternative embodiment, the comprehensive database further includes a plurality of landslide data corresponding to the background data, and the training process of the preset background parameter analysis model includes:

[0015] Obtain remote sensing images before and after historical rainfall events, and establish landslide data corresponding to the background data using visual interpretation method based on the remote sensing images;

[0016] Train the preset model using the background data and the corresponding landslide data to obtain the preset background parameter analysis model.

[0017] The regional landslide prediction method based on transient rainfall infiltration provided by the present invention uses remote sensing images to determine historical landslide data, and trains and optimizes the preset background parameter analysis model using the background data and the corresponding landslide data, improving the robustness and prediction accuracy of the model.

[0018] In an alternative embodiment, use the preset background parameter analysis model to perform sensitivity analysis on the background data to obtain the sensitivity analysis results of each background data, including:

[0019] Perform rasterization processing on the background data to obtain standard background data;

[0020] Use the preset background parameter analysis model to calculate the corresponding landslide instability coefficient according to the standard background data;

[0021] Add a preset perturbation to each background data and calculate the corresponding perturbed landslide instability coefficient;

[0022] Compare the landslide instability coefficient corresponding to the background data and the perturbed landslide instability coefficient to obtain the sensitivity value of each background data to the landslide instability coefficient, which is used as the sensitivity analysis result of each background data.

[0023] The regional landslide prediction method based on transient rainfall infiltration provided by the present invention identifies key influencing parameters through sensitivity analysis, such as saturated permeability coefficient, cohesion, internal friction angle, etc. By calculating the sensitivity values of the background data to the landslide instability coefficient, the influence of the background parameters on the landslide instability coefficient is quantified, thereby improving the robustness and applicability of the model.

[0024] In an optional implementation manner, a preset background parameter analysis model is used to calculate the corresponding landslide instability coefficient according to the standard background data, including:

[0025] Use a preset simulated landslide prediction model to calculate the influence of transient rainfall infiltration on the groundwater piezometric head;

[0026] According to the influence of transient rainfall infiltration on the groundwater piezometric head, combined with the standard background data, use a preset instability coefficient calculation formula to calculate the corresponding landslide instability coefficient.

[0027] The regional landslide prediction method based on transient rainfall infiltration provided by the present invention has optimized the geotechnical parameters specifically, especially considering the influence of regional geotechnical characteristics and transient rainfall infiltration, making the preset background parameter analysis model have stronger applicability in specific geological types. By introducing the transient rainfall infiltration process and dynamically simulating the influence of rainfall on the hydraulic conditions of the slope, it makes up for the deficiency that traditional empirical models cannot consider the instantaneous influence of rainfall, and improves the accuracy of landslide instability prediction.

[0028] In an optional implementation manner, a preset rainfall parameter analysis is performed on the area to be studied to obtain the rainfall parameter threshold of the area to be studied, including:

[0029] Divide the area to be studied into multiple grid cells and initialize the background data of each grid cell;

[0030] Adjust the rainfall intensity and rainfall time of each grid cell according to the preset adjustment rules, and use the preset background parameter analysis model to calculate the real-time landslide instability coefficients corresponding to different rainfall intensities and different rainfall times;

[0031] Determine the rainfall duration threshold corresponding to different rainfall intensities of each grid cell according to the real-time landslide instability coefficient.

[0032] The regional landslide prediction method based on transient rainfall infiltration provided by the present invention explores the specific influence of each rainfall characteristic on slope instability through detailed quantitative analysis of different rainfall characteristics (rainfall intensity, rainfall duration), provides accurate rainfall input parameters for the construction of the landslide threshold curve, and is conducive to improving the accuracy of landslide prediction.

[0033] In an alternative embodiment, the rainfall intensity and rainfall time of each grid unit are adjusted according to a preset adjustment rule, and a preset background parameter analysis model is used to calculate the real-time landslide instability coefficient corresponding to different rainfall intensities and different rainfall times, including:

[0034] Select the lower limit of the preset rainfall intensity range as the initial rainfall intensity, adjust the rainfall duration of each grid unit according to the preset rainfall time adjustment step within the preset rainfall duration range, and use the preset background parameter analysis model to calculate the real-time landslide instability coefficient corresponding to different rainfall times;

[0035] Until the real-time landslide instability coefficient is less than 1, record the rainfall duration threshold corresponding to the initial rainfall intensity;

[0036] Adjust the rainfall intensity according to the preset rainfall intensity adjustment step, and repeat the steps of adjusting the rainfall duration of each grid unit according to the preset rainfall time adjustment step within the preset rainfall duration range, and using the preset background parameter analysis model to calculate the real-time landslide instability coefficient corresponding to different rainfall times, and obtain the rainfall duration threshold corresponding to different rainfall intensities according to the real-time landslide instability coefficient.

[0037] The regional landslide prediction method based on transient rainfall infiltration provided by the present invention adjusts the rainfall intensity and rainfall duration respectively according to the preset rainfall intensity adjustment step and the preset rainfall time adjustment step, simulates the actual rainfall scenario, and obtains the rainfall duration threshold corresponding to different rainfall intensities by calculating the landslide instability coefficient of the grid unit, and the result is more accurate, improving the accuracy of the regional landslide threshold curve.

[0038] In an alternative embodiment, a regional landslide threshold curve is constructed according to the rainfall parameter threshold of the area to be studied, including:

[0039] Fit a rainfall threshold curve according to the rainfall duration thresholds corresponding to each grid unit under different rainfall intensities;

[0040] Use the least squares method to calculate the parameter values of the rainfall threshold curve, and construct a regional landslide threshold curve according to the parameter values and the rainfall duration corresponding to each grid unit under different rainfall intensities.

[0041] The regional landslide prediction method based on transient rainfall infiltration provided by the present invention constructs an adjustable regional rainfall threshold curve for different geological types and rainfall conditions, establishes a landslide prediction model suitable for different geological units by fitting the relationship between rainfall intensity and duration, and solves the adaptability problem of the regional model.

[0042] In a second aspect, the present invention provides a regional landslide prediction device based on transient rainfall infiltration, and the device includes:

[0043] Database construction module, used to construct a comprehensive database of rainfall-induced group landslides, which includes multiple background data;

[0044] A sensitivity analysis module is used to perform sensitivity analysis on background data using a preset background parameter analysis model to obtain sensitivity analysis results of each background data;

[0045] The threshold curve construction module is used to analyze the preset rainfall parameters of the study area, obtain the rainfall parameter threshold of the study area, and construct the regional landslide threshold curve according to the rainfall parameter threshold of the study area;

[0046] The landslide prediction module is used to predict the landslide instability coefficient of the target area based on the sensitivity analysis results and the regional landslide threshold curve. The landslide instability coefficient is used to characterize the possibility of landslide in the target area.

[0047] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0048] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to cause a computer to execute the method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 is a flow chart of a regional landslide prediction method based on transient rainfall infiltration according to an embodiment of the present invention;

[0051] Figure 2 is a flow chart of another method for predicting regional landslides based on transient rainfall infiltration according to an embodiment of the present invention;

[0052] Figure 3 is a schematic diagram of stability coefficient prediction results in a regional landslide prediction method based on transient rainfall infiltration according to an embodiment of the present invention;

[0053] Figure 4It is a structural block diagram of a regional landslide prediction device based on transient rainfall infiltration according to an embodiment of the present invention;

[0054] Figure 5 It is a schematic hardware structure diagram of a computer device according to an embodiment of the present invention. Specific embodiments

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0056] The embodiments of the present invention provide a regional landslide prediction method based on transient rainfall infiltration. By performing sensitivity analysis on the area to be studied based on transient rainfall infiltration and constructing a threshold curve, the effect of accurately predicting rainfall-induced landslides can be achieved. By introducing physical models and uncertainty analysis, the deficiencies of traditional empirical models in prediction accuracy and regional adaptability are overcome, and the impacts of hydrological parameters, geotechnical mechanical parameters, and rainfall characteristics on landslide instability can be considered more comprehensively. Thus, a more robust and region-specific rainfall landslide threshold curve is established, providing a scientific basis for landslide monitoring, early warning, and risk assessment in the region.

[0057] According to an embodiment of the present invention, an embodiment of a regional landslide prediction method based on transient rainfall infiltration is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0058] In this embodiment, a regional landslide prediction method based on transient rainfall infiltration is provided, which can be used in the above computer system. Figure 1 It is a flowchart of a regional landslide prediction method based on transient rainfall infiltration according to an embodiment of the present invention. As Figure 1 shown, the process includes the following steps:

[0059] Step S101, construct a comprehensive database of rainfall-induced mass landslides, and the comprehensive database includes multiple background data.

[0060] Specifically, landslide prediction needs to be analyzed based on sufficient data, so a comprehensive database of rainfall-type group landslides is established. The comprehensive database includes two categories: background database and landslide database. The background database includes five types of data: soil parameters, topographic data, soil type, land use and cover type, and lithology information data:

[0061] (1) Soil parameters: key data such as soil mechanical parameters and permeability coefficient. The attribute fields include:

[0062] Geological age: The geological age of the recorded sample, such as "Quaternary" or "Tertiary";

[0063] Cohesion: cohesion value of soil sample;

[0064] Internal friction angle: the internal friction angle of the soil sample;

[0065] Soil weight: weight of soil sample;

[0066] Saturated permeability coefficient: saturated permeability coefficient value;

[0067] Hydraulic diffusion coefficient: a physical parameter of the ability of water to diffuse in a medium.

[0068] (2) Topographic information: data related to topography, such as slope, soil thickness, etc. The attribute fields include:

[0069] Slope: Slope value extracted based on elevation data;

[0070] Soil thickness: The soil thickness value is estimated based on an empirical formula and ranges from 0.5m to 5m.

[0071] (3) Soil type: soil type distribution in the study area. The attribute fields include:

[0072] Soil type: soil type, such as loam or sandy clay loam;

[0073] Distribution area: the area where this soil type is distributed (such as North, Central or South);

[0074] (4) Land use and land cover type: The resolution (10m) of LULC (the land-use / land-cover) data is recorded.

[0075] (5) Lithology information: records the lithology data of the study area, which is derived from the geological map at a scale of 1:200000. The attribute fields include: Lithology type: the specific type of lithology.

[0076] Each background data can be obtained using ArcGIS (Geographic Information System software). The layers of each influencing factor after rasterization processing recorded in ArcGIS include: the unique number of each raster data, the specific layer type (such as slope, soil thickness, lithology data, etc.), the resolution of the rasterized data, etc. This is only an example and is not limited thereto.

[0077] Step S102: Perform a sensitivity analysis on the background data using a preset background parameter analysis model to obtain the sensitivity analysis results of each background data.

[0078] Specifically, perform a sensitivity analysis on each parameter in the background data using a preset background parameter analysis model to quantify the impact of changes in different hydrological and geotechnical parameters on slope stability. For the soil types unique to the region, by simulating different soil properties and water contents, explore the impact of each parameter on slope stability. The sensitivity analysis helps to identify the key parameters that have a greater impact on the landslide risk and improve the reliability and accuracy of the preset background parameter analysis model.

[0079] Step S103: Conduct a preset rainfall parameter analysis on the area to be studied to obtain the rainfall parameter thresholds of the area to be studied, and construct a regional landslide threshold curve based on the rainfall parameter thresholds of the area to be studied.

[0080] Specifically, based on the background data of the area to be studied, set different rainfall scenarios, and calculate the impact of transient rainfall infiltration and steady-state rainfall infiltration on slope stability. The rainfall parameters include rainfall intensity and rainfall duration. The difference between different rainfall scenarios lies in the different rainfall intensity and / or rainfall duration. To ensure the comparability of each rainfall scenario, only change the rainfall intensity or rainfall duration each time. To ensure the accuracy of the research results, divide the area to be studied into multiple grid cells, and analyze the preset rainfall parameters for each grid cell as a research unit to determine the impact of different rainfall parameters on slope stability.

[0081] Step S104: Predict the landslide instability coefficient of the target area according to the sensitivity analysis results and the regional landslide threshold curve. The landslide instability coefficient is used to characterize the likelihood of a landslide occurring in the target area, and is also called the slope stability safety factor, which is defined as the ratio of the anti-sliding force to the sliding force along the assumed slip surface. When this ratio is greater than 1, the slope is stable; when it is equal to 1, the slope is in a limit equilibrium state; when it is less than 1, the slope will be damaged. By calculating the landslide instability coefficient, it is possible to accurately predict whether there is a risk of landslide in the target area.

[0082] Specifically, for the target area where landslide prediction is required, obtain the background data of the target area, perform sensitivity analysis based on the background data of the target area, and combine the regional landslide threshold curve to predict the landslide instability coefficient of the target area. The landslide vertical stripe coefficient is used to characterize the likelihood of a landslide occurring in the target area.

[0083] The regional landslide prediction method based on transient rainfall infiltration provided in this embodiment determines the key parameters affecting landslides through sensitivity analysis of multiple background data, improves the robustness and adaptability of the preset background parameter analysis model by quantifying the influence of key parameters on the landslide instability coefficient, constructs an adjustable regional rainfall threshold curve through rainfall parameter analysis, and adds uncertainty analysis during model verification to ensure the stability and accuracy of landslide prediction under different geological and rainfall conditions.

[0084] In this embodiment, a regional landslide prediction method based on transient rainfall infiltration is provided, which can be used in the above computer system. Figure 2 It is a flowchart of the regional landslide prediction method based on transient rainfall infiltration according to an embodiment of the present invention, as Figure 2 shown, and this process includes the following steps:

[0085] Step S201, construct a comprehensive database for rainfall-induced mass landslides, and the comprehensive database includes multiple background data. For details, please refer to Figure 1 Step S101 of the embodiment shown, which will not be elaborated here.

[0086] Step S202, perform sensitivity analysis on the background data using a preset background parameter analysis model to obtain the sensitivity analysis results of each background data.

[0087] Specifically, the above step S202 includes:

[0088] Step S2021, rasterize the background data to obtain standard background data.

[0089] Specifically, use ArcGIS software to rasterize the background data to obtain standard background data in the form of a layer. Each background data is rasterized according to the same standard, which is convenient for subsequent unified processing and calculation. The specific process of rasterizing the background data using ArcGIS software is a mature existing technology and will not be elaborated here.

[0090] Step S2022, use the preset background parameter analysis model to calculate the corresponding landslide instability coefficient according to the standard background data.

[0091] In some optional implementation manners, the above step S2022 includes:

[0092] Step a1, calculate the impact of transient rainfall infiltration on the groundwater piezometric head using a preset simulated landslide prediction model.

[0093] Specifically, the TRIGRS model (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model) is a model for simulating the spatio-temporal prediction of shallow rainfall-induced landslides. The infiltration model of the TRIGRS model is a simplified analytical solution based on the Richard equation, which is divided into two parts: transient rainfall infiltration and steady-state rainfall infiltration. Transient infiltration is the core of the model, used to simulate the dynamic changes of pore water pressure within a short time caused by rainfall. The greater the rainfall intensity, the more significant the transient rainfall infiltration effect, which directly affects the stability of the slope. Steady-state rainfall infiltration mainly reflects the long-term changes in the groundwater level of the slope and usually does not cause slope failure. The impact of transient rainfall infiltration on the groundwater piezometric head is expressed by the following formula:

[0094]

[0095] where φ represents the groundwater piezometric head (piezometric head is short for pressure head), used to characterize transient pressure changes; t represents the rainfall time; Z represents the thickness of the soil mass along the vertical direction below the ground surface; d represents the buried depth of the vertical groundwater level under steady state; β = cos 2 δ - (I ZLT / K s ), δ represents the slope; Ks represents the saturated hydraulic conductivity along the vertical direction; I ZLT represents the steady surface flux; I nZ represents the surface infiltration or rainfall intensity corresponding to the nth time interval, that is, the real-time rainfall intensity in the nth time interval; D 1 = D 0 / cos 2 δ, D 0 represents the hydraulic diffusivity; N represents the total number of time intervals; H(t - t n ) represents the Heaviside step function, t n represents the time of the nth time interval in the rainfall infiltration sequence, that is, the rainfall duration in the nth time interval; d LZ represents the vertical depth of the impermeable base boundary.

[0096] Step a2, according to the impact of transient rainfall infiltration on the groundwater piezometric head, combined with the standard background data, calculate the corresponding landslide instability coefficient using a preset instability coefficient calculation formula.

[0097] Specifically, the landslide instability coefficient is used to measure the stability of the slope and can also be called the slope stability safety coefficient. The preset instability coefficient calculation formula is as follows:

[0098]

[0099] Among them, FS represents the landslide instability coefficient, which is used to characterize the safety and stability; c ′ represents the cohesion of the rock and soil mass; represents the friction angle; γ s represents the unit weight of the soil mass; γ w represents the unit weight of the groundwater.

[0100] The regional landslide prediction method based on transient rainfall infiltration provided by the present invention optimizes the rock and soil parameters specifically, especially considering the influence of regional rock and soil characteristics and transient rainfall infiltration, making the preset background parameter analysis model have stronger applicability in specific geological types. By introducing the transient rainfall infiltration process, it dynamically simulates the influence of rainfall on the hydraulic conditions of the slope, makes up for the deficiency that the traditional empirical model cannot consider the instantaneous influence of rainfall, and improves the accuracy of landslide instability prediction.

[0101] Step S2023: Add preset perturbations to each background data and calculate the corresponding perturbed landslide instability coefficient.

[0102] Specifically, to evaluate the sensitivity of each background data to the landslide instability coefficient, small perturbations are respectively added to each background data and the corresponding landslide instability coefficient is calculated as the perturbed landslide instability coefficient. For example, the perturbation amplitude can be ±10%, that is, each parameter is increased or decreased by 10% to calculate the corresponding perturbed landslide instability coefficient. It should be noted that to ensure the accuracy of the sensitivity evaluation results, only one background data is perturbed each time.

[0103] Step S2024: Compare the landslide instability coefficient corresponding to the background data and the perturbed landslide instability coefficient to obtain the sensitivity value of each background data to the landslide instability coefficient as the sensitivity analysis result of each background data.

[0104] Specifically, by comparing the landslide instability coefficient corresponding to the background data and the perturbed landslide instability coefficient after adding perturbations, the difference between the landslide instability coefficient and the perturbed landslide instability coefficient can be used as the sensitivity value of the corresponding background data to the landslide instability coefficient, just as an example. But it is not limited thereto.

[0105] The regional landslide prediction method based on transient rainfall infiltration provided by this embodiment identifies key influencing parameters, such as the saturated permeability coefficient, cohesion, internal friction angle, etc., through sensitivity analysis. By calculating the sensitivity value of the background data to the landslide instability coefficient, the influence of the background parameters on the landslide instability coefficient is quantified, thereby improving the robustness and applicability of the model.

[0106] In some alternative embodiments, the comprehensive database further includes a plurality of landslide data corresponding to the background data, and the training process of the preset background parameter analysis model includes:

[0107] Obtain remote sensing images before and after historical rainfall events, and establish landslide data corresponding to the background data based on the remote sensing images using visual interpretation methods.

[0108] Specifically, based on the remote sensing images before and after historical rainfall events, use visual interpretation methods to establish landslide data corresponding to the background data. The attribute fields of the landslide data include landslide area, landslide perimeter, etc., which are only examples and not limited thereto. The landslide data is the real data when a landslide occurs in the actual scenario corresponding to the background data.

[0109] Use the background data and the corresponding landslide data to train the preset model to obtain a preset background parameter analysis model.

[0110] Specifically, according to the background data, the preset background parameter analysis model can predict landslide data. Use the landslide data corresponding to the background data to train the preset model to improve the accuracy and applicability of the model. By substituting the adjusted background data as parameters into formulas (1) and (2), calculate the predicted value of the landslide instability coefficient FS. Then compare the predicted value of the landslide instability coefficient FS with the actual landslide data when a landslide actually occurs. Adjust and optimize the parameters according to the comparison results to make the predicted value of the landslide instability coefficient FS predicted by the preset background parameter analysis model closer to the actual situation. The specific process of model training is a mature existing technology and will not be elaborated here.

[0111] The regional landslide prediction method based on transient rainfall infiltration provided in this embodiment uses remote sensing images to determine historical landslide data, and uses the background data and the corresponding landslide data to train and optimize the preset background parameter analysis model, improving the robustness and prediction accuracy of the model.

[0112] Step S203: Perform preset rainfall parameter analysis on the area to be studied to obtain the rainfall parameter threshold of the area to be studied, and construct a regional landslide threshold curve according to the rainfall parameter threshold of the area to be studied.

[0113] Specifically, the above step S203 includes:

[0114] Step S2031: Divide the area to be studied into multiple grid cells, and initialize the background data of each grid cell.

[0115] Specifically, the area to be studied is divided into multiple grid cells, and the size of each grid cell is the same as that of each influencing factor layer after rasterization by ArcGIS software. The actual values of the background data of each grid cell are determined through actual measurement. For each grid cell, the actual values of the initial terrain and soil parameters are actually measured, including cohesion, internal friction angle, soil unit weight, saturated permeability coefficient, slope angle, etc., and the background data are initialized using the actual values.

[0116] Step S2032, adjust the rainfall intensity and rainfall duration of each grid cell according to a preset adjustment rule, and calculate the real-time landslide instability coefficient corresponding to different rainfall intensities and different rainfall durations using a preset background parameter analysis model.

[0117] In some optional embodiments, the above step S2032 includes:

[0118] Step b1, select the lower limit of the preset rainfall intensity range as the initial rainfall intensity, adjust the rainfall duration of each grid cell according to the preset rainfall time adjustment step within the preset rainfall duration range, and calculate the real-time landslide instability coefficient corresponding to different rainfall times using a preset background parameter analysis model.

[0119] Specifically, to construct a rainfall threshold curve, it is necessary to calculate the stability of grid cells under different rainfall intensities and rainfall durations. For example, the preset rainfall intensity range is from 1 mm / h to 100 mm / h, and the step size is 5 mm / h. Then the rainfall intensities are set as: 1 mm / h, 5 mm / h, 10 mm / h, 15 mm / h,..., 95 mm / h, 100 mm / h, and the initial rainfall intensity is 1 mm / h; the preset rainfall duration range is from 0 to 60 hours, the step size within the range of 0 to 40 hours is 0.1 hour, and the step size within the range of 40 to 60 hours is 1 hour.

[0120] For each rainfall intensity, repeatedly use the preset background parameter analysis model to calculate the real-time landslide instability coefficient corresponding to different rainfall times. Specifically, it includes selecting the initial rainfall intensity (such as 1 mm / h), and within the preset rainfall duration range (0 to 60 hours), performing iteration according to the preset rainfall time adjustment step. By substituting the real-time rainfall intensity and real-time rainfall duration into the preset background parameter analysis model to calculate the real-time landslide instability coefficient, the stability change of each grid cell under the influence of rainfall is simulated. Among them, the real-time rainfall intensity corresponds to I in formula (1) nZ , and the rainfall duration corresponds to t in formula (1) n .

[0121] Step b2, until the real-time landslide instability coefficient is less than 1, record the rainfall duration threshold corresponding to the initial rainfall intensity.

[0122] Specifically, for each rainfall intensity, during the process of adjusting the rainfall duration, the real-time landslide instability coefficient is iteratively calculated until the real-time landslide instability coefficient is less than 1, indicating that slope instability occurs at this time and a landslide occurs. The rainfall duration when the landslide occurs is used as the rainfall duration threshold corresponding to the initial rainfall intensity.

[0123] It should be noted that the process of calculating the real-time landslide instability coefficient includes: based on the current rainfall duration adjustment step and the current rainfall intensity, calculating the transient pressure change and the change of safety factor through the TRIGRS model to evaluate the impact of rainfall infiltration on slope stability, and calculating the landslide instability coefficient FS of each grid cell using a preset instability coefficient calculation formula.

[0124] Check the value of FS in real time. When FS > 1, the grid cell is considered stable, and the time step is continuously increased for the next iteration; when FS is less than 1 for the first time, record the rainfall duration at this time (i.e., the duration threshold) as the instability duration threshold of the grid cell under the current rainfall intensity.

[0125] Step b3, adjust the rainfall intensity according to the preset rainfall intensity adjustment step, and repeat the step of adjusting the rainfall duration of each grid cell according to the preset rainfall time adjustment step within the preset rainfall duration range, and calculating the real-time landslide instability coefficient corresponding to different rainfall times using the preset background parameter analysis model, and obtaining the rainfall duration threshold corresponding to different rainfall intensities according to the real-time landslide instability coefficient.

[0126] Specifically, adjust the rainfall intensity according to the preset rainfall intensity adjustment step. After each adjustment, there is a corresponding rainfall intensity. Repeat the calculations in steps b1 and b2 to determine the rainfall duration threshold for grid cell instability under different rainfall intensities.

[0127] The regional landslide prediction method based on transient rainfall infiltration provided in this embodiment adjusts the rainfall intensity and rainfall duration respectively according to the preset rainfall intensity adjustment step and the preset rainfall time adjustment step, simulates the actual rainfall scenario, and obtains the rainfall duration threshold corresponding to different rainfall intensities by calculating the landslide instability coefficient of the grid cell, with more accurate results and improved accuracy of the regional landslide threshold curve.

[0128] Step S2033, determine the rainfall duration threshold corresponding to different rainfall intensities of each grid cell according to the real-time landslide instability coefficient.

[0129] Specifically, under different rainfall intensities, during the process of adjusting the rainfall duration from small to large according to the preset rainfall duration step, the rainfall duration when the landslide instability coefficient is less than 1 for the first time is used as the rainfall duration threshold corresponding to this rainfall intensity.

[0130] Step S2034: Fit a rainfall threshold curve based on the rainfall duration thresholds corresponding to each grid cell under different rainfall intensities.

[0131] Specifically, for each grid cell, based on different rainfall intensities and the corresponding rainfall durations, use the method of linear interpolation to fit a rainfall threshold curve. The fitting form of the rainfall threshold curve is as follows.

[0132] I = αD β (3)

[0133] Where, I represents the rainfall intensity (unit: mm / h); D represents the rainfall duration (unit: h); α and β represent fitting parameters. In the rainfall threshold curve, the abscissa represents the rainfall duration, and the ordinate represents the real-time rainfall intensity. This is only an example and is not limited thereto.

[0134] Step S2035: Use the least squares method to calculate the parameter values of the rainfall threshold curve, and construct a regional landslide threshold curve based on the parameter values and the rainfall durations corresponding to each grid cell under different rainfall intensities.

[0135] Specifically, use the least squares method to fit and obtain the values of α and β to best match the data points of rainfall intensity and duration.

[0136] The regional landslide prediction method based on transient rainfall infiltration provided in this embodiment explores the specific impacts of different rainfall characteristics (rainfall intensity, rainfall duration) on slope instability through detailed quantitative analysis, provides accurate rainfall input parameters for the construction of the landslide threshold curve, and is conducive to improving the accuracy of landslide prediction. For different geological types and rainfall conditions, an adjustable regional rainfall threshold curve is constructed. By fitting the relationship between rainfall intensity and duration, a landslide prediction model adapted to different geological units is established, solving the adaptability problem of the regional model.

[0137] Step S204: Predict the landslide instability coefficient of the target area according to the sensitivity analysis results and the regional landslide threshold curve. The landslide instability coefficient is used to characterize the likelihood of a landslide occurring in the target area. For details, please refer to Figure 1 Step S104 of the embodiment shown, which will not be elaborated here.

[0138] Specifically, for a certain area to be studied, carry out the prediction of the stability coefficient under rainfall events based on the physical TRIGRS model, consider the influence of rainfall infiltration on pore pressure and safety factor, and obtain preliminary prediction results by simulating the slope stability under different rainfall intensity and duration scenarios, such as Figure 3As shown, it is a schematic diagram of the prediction results of the stability coefficient under rainfall events based on a physical model. To further improve the accuracy of the prediction results, actual landslide data is used to finely adjust and optimize the physical parameters to ensure the agreement between the model parameters and the actual situation.

[0139] Based on the adjusted physical parameters and the method provided in this embodiment, a rainfall threshold curve for the area to be studied is constructed to ensure that the rainfall threshold curve can truly and accurately reflect the landslide risk level in the area to be studied under different rainfall conditions.

[0140] In this embodiment, a regional landslide prediction device based on transient rainfall infiltration is also provided. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0141] This embodiment provides a regional landslide prediction device based on transient rainfall infiltration, as Figure 4 shown, including:

[0142] A database construction module 401, which is used to construct a comprehensive database of rainfall-induced mass landslides. The comprehensive database includes a plurality of background data.

[0143] A sensitivity analysis module 402, which is used to perform sensitivity analysis on the background data using a preset background parameter analysis model to obtain the sensitivity analysis results of each background data.

[0144] A threshold curve construction module 403, which is used to perform preset rainfall parameter analysis on the area to be studied to obtain the rainfall parameter threshold of the area to be studied, and construct a regional landslide threshold curve according to the rainfall parameter threshold of the area to be studied.

[0145] A landslide prediction module 404, which is used to predict the landslide instability coefficient of the target area according to the sensitivity analysis results and the regional landslide threshold curve. The landslide instability coefficient is used to characterize the likelihood of a landslide occurring in the target area.

[0146] In some alternative implementation manners, the sensitivity analysis module 402 includes:

[0147] A data rasterization unit, which is used to rasterize the background data to obtain standard background data.

[0148] An instability coefficient calculation unit, which is used to calculate the corresponding landslide instability coefficient according to the standard background data using a preset background parameter analysis model.

[0149] A perturbation adding unit for adding a preset perturbation to each background data and calculating the corresponding perturbation landslide instability coefficient.

[0150] A sensitivity analysis unit for comparing the landslide instability coefficient corresponding to the background data and the perturbation landslide instability coefficient to obtain the sensitivity value of each background data to the landslide instability coefficient, which is used as the sensitivity analysis result of each background data.

[0151] In some alternative embodiments, the instability coefficient calculation unit includes:

[0152] A transient rainfall infiltration analysis subunit for calculating the influence of transient rainfall infiltration on the groundwater piezometric head by using a preset simulated landslide prediction model.

[0153] An instability coefficient calculation subunit for calculating the corresponding landslide instability coefficient by using a preset instability coefficient calculation formula according to the influence of transient rainfall infiltration on the groundwater piezometric head and in combination with standard background data.

[0154] In some alternative embodiments, the threshold curve construction module 403 includes:

[0155] A region division unit for dividing the area to be studied into a plurality of grid cells and initializing the background data of each grid cell.

[0156] A rainfall parameter analysis unit for adjusting the rainfall intensity and rainfall time of each grid cell according to a preset adjustment rule and calculating the real-time landslide instability coefficient corresponding to different rainfall intensities and different rainfall times by using a preset background parameter analysis model.

[0157] An instability time threshold calculation unit for determining the rainfall duration threshold corresponding to different rainfall intensities of each grid cell according to the real-time landslide instability coefficient.

[0158] A curve fitting unit for fitting a rainfall threshold curve according to the rainfall duration thresholds corresponding to different rainfall intensities of each grid cell.

[0159] A curve parameter determination unit for calculating the parameter values of the rainfall threshold curve by using the least squares method and constructing a regional landslide threshold curve according to the parameter values and the rainfall duration corresponding to different rainfall intensities of each grid cell.

[0160] In some alternative embodiments, the rainfall parameter analysis unit includes:

[0161] A rainfall parameter adjustment and setting subunit for selecting the lower limit of a preset rainfall intensity range as the initial rainfall intensity, adjusting the rainfall duration of each grid cell according to a preset rainfall time adjustment step within a preset rainfall duration range, and calculating the real-time landslide instability coefficient corresponding to different rainfall times by using a preset background parameter analysis model.

[0162] An initial threshold determination subunit, configured to record the rainfall duration threshold corresponding to the initial rainfall intensity until the real-time landslide instability coefficient is less than 1.

[0163] A loop calculation subunit, configured to adjust the rainfall intensity according to a preset rainfall intensity adjustment step, and repeat the step of adjusting the rainfall duration of each grid unit according to a preset rainfall time adjustment step within a preset rainfall duration range, and calculating the real-time landslide instability coefficient corresponding to different rainfall times by using a preset background parameter analysis model, and obtaining the rainfall duration threshold corresponding to different rainfall intensities according to the real-time landslide instability coefficient.

[0164] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding embodiments above, and will not be repeated here.

[0165] The regional landslide prediction device based on transient rainfall infiltration in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0166] The embodiment of the present invention further provides a computer device having the above-mentioned Figure 4 shown regional landslide prediction device based on transient rainfall infiltration.

[0167] Please refer to Figure 5 , Figure 5 which is a schematic structural diagram of a computer device provided by an optional embodiment of the present invention. As Figure 5 shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other through different buses and can be installed on a common main board or installed in other ways according to needs. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (such as an array of servers, a set of blade servers, or a multi-processor system). Figure 5 One processor 10 is taken as an example in

[0168] The processor 10 may be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 may further include a hardware chip. The above-mentioned hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above-mentioned programmable logic device may be a complex programmable logic device, a field programmable gate array, a generic array logic, or any combination thereof.

[0169] Among them, the memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiments.

[0170] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely provided with respect to the processor 10, and these remote memories may be connected to the computer device through a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0171] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 may also include a combination of the above types of memories.

[0172] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

[0173] The embodiments of the present invention also provide a computer-readable storage medium. The method according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium may also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiments is implemented.

[0174] Although embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A regional landslide prediction method based on transient rainfall infiltration, characterized in that: The method comprises: Constructing a comprehensive database of rainfall-induced group landslides, wherein the comprehensive database includes a plurality of background data; Performing sensitivity analysis on the background data using a preset background parameter analysis model to obtain sensitivity analysis results of each background data; Performing a preset rainfall parameter analysis on the study area to obtain a rainfall parameter threshold of the study area, and constructing a regional landslide threshold curve according to the rainfall parameter threshold of the study area; The landslide instability coefficient of the target area is predicted according to the sensitivity analysis result and the regional landslide threshold curve, and the landslide instability coefficient is used to characterize the possibility of landslide in the target area.

2. The method according to claim 1, characterized in that The comprehensive database also includes a plurality of landslide data corresponding to the background data. The training process of the preset background parameter analysis model includes: Obtaining remote sensing images before and after historical rainfall events, and establishing landslide data corresponding to background data using visual interpretation methods based on the remote sensing images; The preset model is trained using the background data and the corresponding landslide data to obtain a preset background parameter analysis model.

3. The method according to claim 1, characterized in that The background data are subjected to sensitivity analysis using a preset background parameter analysis model to obtain sensitivity analysis results of each background data, including: Performing rasterization processing on the background data to obtain standard background data; Using a preset background parameter analysis model, the corresponding landslide instability coefficient is calculated according to the standard background data; Add preset disturbances to each background data and calculate the corresponding disturbance landslide instability coefficient; By comparing the landslide instability coefficient corresponding to the background data and the disturbed landslide instability coefficient, the sensitivity value of each background data to the landslide instability coefficient is obtained as the sensitivity analysis result of each background data.

4. The method according to claim 3, characterized in that Using the preset background parameter analysis model, the corresponding landslide instability coefficient is calculated according to the standard background data, including: The influence of transient rainfall infiltration on groundwater pressure head is calculated using a preset simulated landslide prediction model; According to the influence of the transient rainfall infiltration on the groundwater pressure head, combined with the standard background data, the corresponding landslide instability coefficient is calculated using a preset instability coefficient calculation formula.

5. The method according to claim 1 or 4, characterized in that: Performing a preset rainfall parameter analysis on the study area to obtain the rainfall parameter threshold of the study area includes: Divide the area to be studied into multiple grid cells and initialize the background data of each grid cell; The rainfall intensity and rainfall time of each grid unit are adjusted according to the preset adjustment rules, and the real-time landslide instability coefficient corresponding to different rainfall intensities and different rainfall times is calculated using the preset background parameter analysis model; The rainfall duration threshold corresponding to different rainfall intensities of each grid unit is determined according to the real-time landslide instability coefficient.

6. The method according to claim 5, characterized in that The rainfall intensity and rainfall time of each grid unit are adjusted according to the preset adjustment rules, and the real-time landslide instability coefficient corresponding to different rainfall intensities and different rainfall times is calculated using the preset background parameter analysis model, including: The lower limit of the preset rainfall intensity range is selected as the initial rainfall intensity, the rainfall duration of each grid unit is adjusted according to the preset rainfall time adjustment step within the preset rainfall duration range, and the real-time landslide instability coefficient corresponding to different rainfall times is calculated using the preset background parameter analysis model; Until the real-time landslide instability coefficient is less than 1, the rainfall duration threshold corresponding to the initial rainfall intensity is recorded; The rainfall intensity is adjusted according to the preset rainfall intensity adjustment step, and the rainfall duration of each grid unit is repeatedly adjusted according to the preset rainfall time adjustment step within the preset rainfall duration range, and the real-time landslide instability coefficient corresponding to different rainfall times is calculated using the preset background parameter analysis model, and the rainfall duration threshold corresponding to different rainfall intensities is obtained according to the real-time landslide instability coefficient.

7. The method according to claim 6, characterized in that A regional landslide threshold curve is constructed according to the rainfall parameter threshold of the area to be studied, including: A rainfall threshold curve is fitted according to the rainfall duration threshold corresponding to each grid unit under different rainfall intensities; The parameter values ​​of the rainfall threshold curve are calculated using the least squares method, and a regional landslide threshold curve is constructed according to the parameter values ​​and the rainfall duration corresponding to each grid unit under different rainfall intensities.

8. A regional landslide prediction device based on transient rainfall infiltration, characterized in that: The device comprises: A database construction module is used to construct a comprehensive database of rainfall-type group landslides, wherein the comprehensive database includes a plurality of background data; A sensitivity analysis module is used to perform sensitivity analysis on the background data using a preset background parameter analysis model to obtain sensitivity analysis results of each background data; A threshold curve construction module is used to analyze the preset rainfall parameters of the study area, obtain the rainfall parameter threshold of the study area, and construct a regional landslide threshold curve according to the rainfall parameter threshold of the study area; The landslide prediction module is used to predict the landslide instability coefficient of the target area according to the sensitivity analysis results and the regional landslide threshold curve. The landslide instability coefficient is used to characterize the possibility of landslide in the target area.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

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