Landslide susceptible area dynamic identification method based on hydrology-landslide coupling model

Abstract

The embodiment of the invention discloses a landslide susceptible area dynamic identification method based on a hydrological-landslide coupling model, and the method comprises the steps: constructing a hydrological model, and carrying out the hydrological process simulation based on the hydrological model; constructing a slope stability model based on the hydrological process; popularizing the slope stability model to a regional scale, and coupling the slope stability model with the hydrological model to obtain a hydrological-landslide coupling model; based on the hydrology-landslide coupling model, combined with landslide influence factors, dynamically identifying a landslide susceptible area, and obtaining an identification result, the landslide influence factors including but not limited to rainfall intensity, gradient, earth surface coverage and soil type; the precision of a regional calculation result of the model can be improved, and the target of simultaneously forecasting flood and landslide disasters is achieved.

Description

Technical Field

[0001] This invention relates to the field of disaster early warning technology, and in particular to a method for dynamic identification of landslide-prone areas based on a hydrological-landslide coupling model. Background Art

[0002] Rainfall-induced floods and landslides are two major natural disasters worldwide, often causing enormous property damage and loss of life. Landslides occur at various soil depths, from shallow to deep, and across multiple time scales. Human activities such as quarrying and urbanization disrupt original vegetation and stable soil systems, also contributing to large-scale landslides. When landslides are located near human settlements or trigger secondary disasters, they threaten human property and lives.

[0003] Due to the complex and diverse geographical features, the region faces the challenge of frequent extreme weather and climate events, such as the coastal areas being frequently hit by strong typhoons, and uneven precipitation distribution with significant differences in time and space. In addition, various engineering activities have an increasing impact on the geological environment, increasing the risk of geological disasters.

[0004] Due to the lack of actual observational data and the ambiguity and complexity of the intrinsic triggering mechanism of precipitation for landslide development, accurately simulating and predicting landslides from multiple temporal and spatial perspectives remains a challenge. Although some studies have proposed slope stability assessment models that consider physical mechanisms, their limitations are also very obvious, namely, they do not describe the impact and contribution of precipitation on slope stability in a more refined physical process. Currently, the main method for predicting floods and landslides at the regional scale is to couple hydrological models and slope stability models, such as TRIGRS and HIRESSS, which have achieved simulation and forecasting at the regional scale. However, due to the existence of regional spatial heterogeneity, environmental factors and geological hazard sensitivities vary greatly in different locations, often leading to significant deviations between model calculations and actual conditions, resulting in low accuracy. Furthermore, the scarcity of driving data prevents the achievement of real-time disaster forecasting. Summary of the Invention

[0005] The main objective of this invention is to provide a dynamic identification method for landslide-prone areas based on a hydrological-landslide coupling model. This method can dynamically identify landslide-prone areas, improve the accuracy of the model's regional calculation results, and achieve the goal of simultaneously forecasting floods and landslide disasters.

[0006] To achieve the above objectives, the first aspect of this application provides a method for dynamic identification of landslide-prone areas based on a hydrological-landslide coupling model, the method comprising:

[0007] Construct a hydrological model and simulate hydrological processes based on the hydrological model;

[0008] A slope stability model is constructed based on the aforementioned hydrological process;

[0009] The slope stability model is extended to a regional scale and coupled with the hydrological model to obtain a hydrological-landslide coupled model.

[0010] Based on the aforementioned hydrological-landslide coupling model, combined with landslide influencing factors, landslide-prone areas are dynamically identified, and identification results are obtained. The landslide influencing factors include, but are not limited to, rainfall intensity, slope, land cover, and soil type.

[0011] Optionally, the construction of the hydrological model includes:

[0012] The infiltration capacity of the remaining precipitation after it is intercepted by the vegetation canopy is analyzed to determine whether there is excessive infiltration runoff. In addition to the excessive infiltration runoff, the precipitation that infiltrates into the soil is divided into runoffs according to the three-layer soil generalization model, taking into account soil evapotranspiration. For each grid, the precipitation is divided according to the infiltration capacity to determine the infiltration capacity curve of the grid.

[0013] The expression for the infiltration volume is determined based on the infiltration capacity curve.

[0014] Optionally, the hydrological process simulation based on the hydrological model includes:

[0015] Based on the aforementioned infiltration volume expression, a linear reservoir is used to simulate the confluence process of surface runoff and groundwater runoff. At each time step, the runoff generation of net surface rainfall and infiltration rainfall is calculated, and then the confluence time of the runoff generation on the upstream grid to the downstream grid is calculated, and its impact on the runoff generation and runoff of the downstream grid is analyzed.

[0016] Optionally, constructing the slope stability model based on the hydrological process includes:

[0017] Establish the expression for the shear stress of a potential landslide at the sliding surface;

[0018] Based on the aforementioned shear stress expression, the force equilibrium relationship of the slope is determined using the ultimate slope stability theory.

[0019] Based on the force balance relationship of the slope and the description of soil infiltration and water content in the hydrological model, the expression for the slope safety factor is obtained.

[0020] Optionally, the step of dynamically identifying landslide-prone areas based on the hydrological-landslide coupling model and incorporating landslide influencing factors includes:

[0021] All hydrological variables and fluxes are calculated using the hydrological model in the hydrological-landslide coupled model.

[0022] The slope safety factor is calculated using the landslide model in the hydrology-landslide coupling model, the hydrological variables and fluxes, and the initial conditions.

[0023] The landslide disaster occurrence rate of each grid is calculated using the landslide disaster occurrence rate expression, which is based on the landslide influencing factor.

[0024] Based on the landslide disaster incidence rate of each grid, the slope safety factor, the safe area and the landslide-prone area, the landslide occurrence threshold and risk level range of each grid are determined.

[0025] Optionally, the expression for the landslide disaster incidence rate is:

[0026]

[0027] Among them, R j k represents the incidence rate of the k-th type of disaster under the given j-th type of variable; K represents the disaster type, where 0 represents a rainfall event that did not cause a landslide disaster, and 1 represents a landslide disaster event caused by rainfall; J represents the category of the variable, including the rainfall intensity, the slope, the surface cover, and the soil type; n(J=j and K=k) represents the number of landslide disaster events caused by rainfall when J belongs to the j-th category; n[J=j and (K=k or K=0)] represents the number of rainfall events when J belongs to the j-th category.

[0028] Optionally, the identification results include forecast results and accuracy verification results; the forecast results include a landslide risk distribution map of a specific area, and the accuracy verification results include an ROC curve, used to evaluate the predictive performance of the model in different months.

[0029] A second aspect of this application provides a dynamic identification device for landslide-prone areas, comprising:

[0030] A construction module is used to build a hydrological model and simulate hydrological processes based on the hydrological model.

[0031] The construction module is also used to construct a slope stability model based on the hydrological process;

[0032] The construction module is also used to extend the slope stability model to a regional scale and couple it with the hydrological model to obtain a hydrological-landslide coupled model.

[0033] The identification module is used to dynamically identify landslide-prone areas based on the hydrological-landslide coupling model and in combination with landslide influencing factors, and obtain identification results. The landslide influencing factors include, but are not limited to, rainfall intensity, slope, land cover and soil type.

[0034] A third aspect of this application provides an electronic device including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform steps as described in the first aspect and any possible implementation thereof.

[0035] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of the method described in the first aspect.

[0036] This application provides a method for dynamic identification of landslide-prone areas based on a hydrological-landslide coupling model. The method involves constructing a hydrological model and simulating hydrological processes based on this model; constructing a slope stability model based on the hydrological processes; extending the slope stability model to a regional scale and coupling it with the hydrological model to obtain a hydrological-landslide coupling model; and dynamically identifying landslide-prone areas based on this model and incorporating landslide influencing factors, including but not limited to rainfall intensity, slope, land cover, and soil type. This method combines four influencing factors—rainfall intensity, land cover type, soil type, and slope—with the hydrological and landslide models to dynamically identify landslide-prone areas, improving the accuracy of regional calculations and achieving the goal of simultaneously forecasting floods and landslide disasters. Attached Figure Description

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0038] in:

[0039] Figure 1 A flowchart illustrating a method for dynamic identification of landslide-prone areas based on a hydrological-landslide coupling model, provided in an embodiment of this application.

[0040] Figure 2 This is a schematic diagram of basic hydrological elements and an infinite slope provided for an embodiment of this application.

[0041] Figure 3 This application provides a schematic diagram illustrating the forecast results and accuracy verification results of long-term small river floods and flash floods in a certain region, as well as the forecast results of accuracy verification.

[0042] Figure 4This is a schematic diagram illustrating the results of long-term geological disaster forecasting and accuracy verification in a certain region, provided in an embodiment of this application.

[0043] Figure 5 This application provides a schematic diagram illustrating a case study of a flood disaster in County A caused by a rainstorm, as illustrated in an embodiment of the present application.

[0044] Figure 6 This is a schematic diagram illustrating a case study of a flash flood and debris flow disaster in County B caused by a rainstorm, provided in an embodiment of this application.

[0045] Figure 7 This application provides a schematic diagram illustrating a case study of flooding and geological disasters in City C caused by a rainstorm, as part of an embodiment of the present application.

[0046] Figure 8 This is a schematic diagram of the structure of a dynamic identification device for landslide-prone areas provided in an embodiment of this application;

[0047] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0048] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0049] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0050] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0051] The embodiments of this application are described below with reference to the accompanying drawings.

[0052] Please see Figure 1 The above is a flowchart illustrating a method for dynamic identification of landslide-prone areas based on a hydrological-landslide coupling model, as provided in an embodiment of this application. Figure 1 As shown, the method includes:

[0053] 101. Construct a hydrological model and simulate hydrological processes based on the above hydrological model.

[0054] The hydrological model mentioned in this application embodiment is a mathematical modeling method that can predict and simulate hydrological changes within a watershed by describing and analyzing the hydrological cycle process.

[0055] In one alternative implementation, the above-mentioned construction of the hydrological model includes:

[0056] The infiltration capacity of the remaining precipitation after it is intercepted by the vegetation canopy is analyzed to determine whether there is excessive infiltration runoff. In addition to the above-mentioned excessive infiltration runoff, the precipitation infiltrating into the soil is divided into runoffs according to the three-layer soil generalization model, taking into account soil evapotranspiration. For each grid, the precipitation is divided according to the above infiltration capacity to determine the infiltration capacity curve of the grid.

[0057] The expression for the infiltration volume is determined based on the above infiltration capacity curve.

[0058] The infiltration capacity curve mentioned in the embodiments of this application is used to describe the process of soil infiltration capacity (infiltration rate) changing over time under conditions of sufficient water supply. It is crucial for understanding and predicting how rainfall is converted into runoff and the soil's ability to absorb water.

[0059] Specifically, hydrological models can simulate the spatiotemporal variations of water volume and energy flux in each grid within a watershed. The core components include runoff generation and runoff concentration. Precipitation is first intercepted by the vegetation canopy; any remaining precipitation is assessed based on infiltration capacity to determine if there is excess runoff. Besides the excess runoff, precipitation infiltrating into the soil is classified into runoff based on a three-layer soil generalization model, taking into account soil evapotranspiration. The infiltration capacity curve can be represented as follows:

[0060]

[0061] Where i represents the grid infiltration capacity; i m The maximum infiltration capacity of the grid is a function of the maximum water storage capacity of the soil; A is the proportion of the runoff-generating area of ​​the i-th grid; b i represents the shape parameters of the curve.

[0062] Therefore, the amount of water that infiltrates can be calculated using the following formula:

[0063]

[0064] Where: I represents the amount of water infiltrating; W m The maximum water storage capacity is represented by W; the average moisture content of the three soil layers is represented by P. soil This refers to clean rain that falls onto the soil surface.

[0065] In one optional implementation, the above-mentioned hydrological process simulation based on the hydrological model includes:

[0066] Based on the above expression for infiltration volume, a linear reservoir is used to simulate the confluence process of surface runoff and groundwater runoff. At each time step, the runoff generation of net surface rainfall and infiltration rainfall is calculated respectively. Then, the confluence time of the runoff generation on the upstream grid to the downstream grid is calculated, and its impact on the runoff generation and runoff of the downstream grid is analyzed.

[0067] The linear reservoir model mentioned in this application embodiment can describe hydrological processes by simulating the linear relationship between the reservoir's storage capacity and outflow. In hydrological models, linear reservoir models are typically used to simulate the confluence process of surface runoff and groundwater runoff. At each time step, the runoff generation of net surface rainfall and infiltration rainfall is calculated separately, and then the confluence time of the runoff generation on the upstream grid to the downstream grid is calculated, and its impact on the runoff generation and runoff of the downstream grid is analyzed.

[0068] 102. Based on the above hydrological processes, construct a slope stability model.

[0069] Hydrological processes alter soil moisture, weight, groundwater level, and other factors. Almost all hydrological processes affect slope stability and form the theoretical basis for model coupling.

[0070] Figure 2 This is a schematic diagram of basic hydrological elements and an infinite slope provided for an embodiment of this application. Figure 2 The explanations of each element are as follows:

[0071] Rainfall (heavy rain): The arrow pointing downwards from the clouds in the diagram represents rainfall, which is an important hydrological factor affecting slope stability. Rainfall increases soil moisture, thereby affecting pore water pressure and soil shear strength.

[0072] The vadose zone is the area below the Earth's surface but above the water table, where soil moisture can flow freely. Changes in moisture content in this area directly affect slope stability.

[0073] Slip surface: The potential slip surface within a slope is crucial for slope stability analysis. Under the influence of rainfall or other factors, the slope may slide along this surface.

[0074] Saturated zone: The area below the water table, where the pores between soil particles are filled with water. Changes in the water level in this area can affect the stability of the slope.

[0075] Groundwater level: The interface between groundwater and the atmosphere. A rise or fall in the groundwater level affects the pore water pressure and stability of a slope.

[0076] Bedrock layer: The rock layer located below the saturation zone, forming the base of the slope. The geological characteristics of the bedrock layer also affect the stability of the slope.

[0077] Slope (α): The angle of inclination of a slope, which is an important factor affecting slope stability. The steeper the slope, the more prone it is to landslides.

[0078] Height (H): The height of the slope, the vertical distance from the bedrock to the ground surface.

[0079] Sliding surface depth (ΔS): The distance from the sliding surface to the bedrock layer. This depth affects the stability of the slope.

[0080] In one optional implementation, step 102 includes:

[0081] Establish the expression for the shear stress of a potential landslide at the sliding surface;

[0082] Based on the above shear stress expression, the force equilibrium relationship of the slope is determined using the ultimate slope stability theory.

[0083] Based on the above-mentioned force balance relationship of the slope and the description of infiltration and water content of multi-layer soil in the above-mentioned hydrological model, the expression for the slope safety factor is obtained.

[0084] Shear stress at the sliding surface of a potential landslide is one of the key factors affecting slope stability. The magnitude of shear stress depends on various factors, including soil physical properties, moisture conditions, and external loads. In stability analysis, the calculation of shear stress typically involves the principle of minimum potential energy, and the shear stress can be determined by solving for the condition of minimum potential energy.

[0085] The magnitude of shear stress directly affects slope stability. When the shear stress exceeds the shear strength of the soil, the slope may become unstable. Therefore, the force equilibrium relationship of the slope can be determined based on shear stress, thereby obtaining the expression for the slope safety factor.

[0086] Soil moisture-stress relationship is an important concept in geotechnical engineering, describing the mechanical behavior of soil under different moisture conditions. This relationship is crucial for understanding and predicting slope stability, soil compressibility, and soil strength. Specifically, based on the soil moisture-stress relationship, potential landslides occur at the sliding surface (see...). Figure 2 The shear stress of ) can be expressed as:

[0087] τ f =c′+(σ-u a )tgφ′+(u a -u w )tgφ b (3)

[0088] Where c′ is the effective soil viscosity; φ′ is the soil internal friction angle; tgφ b The coefficient representing the relationship between soil stress and matrix suction is given by σ, where σ is the total soil stress, and u is the total soil stress. w It is the pore water pressure, u a It is atmospheric pressure, which can usually be considered as 0.

[0089] Hydrological processes alter soil moisture, which in turn affects pore water pressure and soil cohesion (effective viscosity). When rainfall increases soil moisture, pore water pressure rises, reducing the effective stress between soil particles and thus lowering the soil's shear strength. Simultaneously, soil cohesion may also decrease due to moisture, both of which contribute to increased shear stress and potentially trigger landslides.

[0090] Furthermore, according to the ultimate slope stability theory, at the critical state, the force equilibrium relationship of the slope is as follows:

[0091]

[0092] In the formula, W is the weight of the soil slope per unit width; γ is the slope; therefore, the slope safety factor F s It can be represented as:

[0093]

[0094] Based on the description of multi-layered soil infiltration and water content in the above hydrological model, F s This can be further expressed as:

[0095]

[0096] In the formula, γ d denoted as soil dry bulk density; H is the total soil thickness; θ(Z) is the soil moisture distribution along the longitudinal direction; u w (Z) represents the longitudinal component of soil pore water pressure. Integrating θ(Z) provides a more accurate representation of soil weight in multi-layered scenarios. By varying the value of Z, the stability of the sliding surface at different depths can be determined.

[0097] 103. Extend the above slope stability model to the regional scale and couple it with the above hydrological model to obtain the hydrological-landslide coupled model.

[0098] Specifically, by extending the aforementioned slope stability model to a regional scale and fully coupling it with a hydrological model, a coupled simulation framework can be obtained. At the regional scale, more factors are considered, including topography, geology, land use, soil properties, and hydrological conditions, which requires the model to handle larger-scale data and more complex interactions.

[0099] 104. Based on the above hydrological-landslide coupling model and combined with landslide influencing factors, landslide-prone areas are dynamically identified and identification results are obtained. The above landslide influencing factors include, but are not limited to, rainfall intensity, slope, land cover and soil type.

[0100] In one optional implementation, the above-mentioned hydrological-landslide coupling model, combined with landslide influencing factors, dynamically identifies landslide-prone areas, including:

[0101] All hydrological variables and fluxes are calculated using the hydrological model in the above-mentioned hydrological-landslide coupled model.

[0102] The slope safety factor is calculated using the landslide model in the above hydrological-landslide coupling model, the above hydrological variables and fluxes, and the initial conditions.

[0103] The landslide disaster occurrence rate of each grid is calculated using the landslide disaster occurrence rate expression, which is based on the landslide influencing factors mentioned above.

[0104] Based on the landslide disaster incidence rate of each grid, the slope safety factor, the safe area and the landslide-prone area, the landslide occurrence threshold and risk level range of each grid are determined.

[0105] All hydrological variables and fluxes can be calculated using a hydrological model, including vegetation interception, precipitation infiltration, runoff generation, runoff confluence, and re-infiltration of surface runoff, and the initial soil moisture content of the region can be provided. Then, the stability of the slope can be calculated using these hydrological variables and initial conditions provided by the hydrological model through a landslide model.

[0106] It should be noted that in practical applications, the above-mentioned hydrological model can be used for simulation to predict flood disasters; the hydrological-landslide coupled model can be used to predict landslide disasters; and both flood and landslide disasters can be predicted simultaneously.

[0107] This application also proposes a dynamic identification of landslide-prone areas based on the combined influence of multiple factors. The study selects rainfall intensity (I), slope (θ), land cover (L), and soil type (S) as landslide influencing factors and analyzes their relationship with landslide events.

[0108] Specifically, the occurrence rate of landslide disasters can be calculated by defining the relevant events as needed and conducting data statistics and analysis.

[0109] For example, in this application, a rainfall event is defined as continuous rainfall or rainfall with intervals not exceeding 2 hours ending at the time of landslide occurrence, and the rainfall intensity is calculated according to this definition. For landslide disaster event data, it is first projected onto a DEM with a spatial resolution of 1×1km. If multiple landslides occur simultaneously on the same grid, these landslide disasters are considered to belong to a single landslide event. Secondly, the grid location, rainfall intensity, rainfall intensity level, slope, land cover, and soil type of these landslide events are statistically analyzed to create a statistical table of landslide disaster event attributes. Finally, grids where no rainfall or landslide events occurred are removed, and the landslide disaster occurrence rate is calculated.

[0110] In one optional implementation, the expression for the landslide disaster incidence rate is:

[0111]

[0112] Among them, R j ,k is the incidence rate of the k-th type of disaster under the given j-th type of variable; K is the disaster type, where 0 represents a rainfall event that did not cause a landslide disaster, and 1 represents a landslide disaster event caused by rainfall; J is the category of the variable, including the above rainfall intensity, the above slope, the above surface cover, and the above soil type; n(J=j and K=k) represents the number of landslide disaster events caused by the above rainfall when J belongs to the j-th category; n[J=j and (K=k or K=0)] represents the number of the above rainfall events (including those that caused landslides and those that did not) when J belongs to the j-th category.

[0113] Optionally, landslide safety zones and landslide-prone zones can be identified based on the landslide disaster incidence rate, and the landslide occurrence threshold and risk level range for each grid can be determined. The risk level range for prone zones can be determined based on the aforementioned slope safety system; however, the specific judgment rules in this application's embodiments are not limited.

[0114] Further optionally, the above identification results include forecast results and accuracy verification results; the above forecast results include a landslide risk distribution map of a specific area, and the above accuracy verification results include an ROC curve, used to evaluate the predictive performance of the model in different months.

[0115] Specifically, in this application embodiment, forecast results can be generated, which can be displayed as a landslide risk distribution map of a specific area, or as a flood disaster distribution map of a specific area; in addition, corresponding accuracy verification results can also be generated, which can be displayed through ROC curves or other forms.

[0116] ROC (Receiver Operating Characteristic) curves are commonly used to evaluate the performance of classification models.

[0117] Figure 3 This diagram illustrates the results of long-term small river flood and flash flood forecasts and their accuracy verification in a certain region, as provided in this application embodiment. Figure 3 As shown, the specific explanation is as follows:

[0118] First, the left image is a map generated by a Geographic Information System (GIS), showing the landslide risk distribution in a specific area. Different colors are used to represent landslide risk levels; darker colors indicate higher landslide risk. The left image also marks historical landslide locations (represented by circles and numbers) and some geographical features, such as rivers. The right image is a ROC curve used to evaluate the performance of landslide risk prediction models for different months. The horizontal axis (FPR) represents the false positive rate, i.e., the proportion of safe areas incorrectly predicted as high-risk landslide areas; the vertical axis (TPR) represents the true positive rate, i.e., the probability of correctly predicting high-risk landslide areas. The right image uses different colored curves to represent model performance for different months, and the AUC value of each curve represents the overall prediction accuracy of the model for that month. The closer the AUC value is to 1, the better the model's prediction performance.

[0119] Figure 3 The model's flood forecast results for a specific region over a long period of several months show that high-value areas of discharge are relatively dispersed and distributed across various regions, with particularly severe flooding near river channels. Overall, the reported flood locations in most areas fall within the grid cells with higher calculated discharge values. The simulation forecast performance was analyzed using ROC-AUC, with AUC values ​​exceeding 0.8 from May to August, and reaching 0.85 in July, indicating high accuracy in flood forecasting. However, it was also found that the model underestimated some high-risk flood areas and made false alarms regarding flooding in some regions, allowing for timely improvements in future studies.

[0120] The identification results provide a clear picture of areas with high landslide risk and the accuracy of corresponding model predictions. For example, if the left graph shows a high landslide risk in a certain area, and the AUC value for the corresponding month in the right graph is also high, then the model's predictions can be relied upon with greater confidence. This approach, combining geospatial analysis and model performance evaluation, can help decision-makers better understand landslide risks and take appropriate preventative measures.

[0121] akin, Figure 4This is a schematic diagram illustrating the results of a long-term geological disaster forecast and its accuracy verification in a certain region, provided as an embodiment of this application. Figure 4 As shown, most areas with high landslide risk are mainly distributed in the northwest, with a smaller portion located in the northeast and central regions. While the landslide risk is lower in the southern region, it still presents some risk. High-risk areas, due to their loose soil texture and steep slopes, are highly susceptible to landslides, debris flows, and collapses after prolonged rainfall during the flood season, as the soil is often saturated. Statistics show that from May to August, this region experienced 105 landslide and debris flow incidents, with July accounting for the highest number at 46. The accuracy evaluation index (AUC) values ​​for May to August were 0.72, 0.71, 0.67, and 0.72, respectively, with an overall accuracy exceeding 0.7, indicating landslide prediction capability. However, some areas still experienced missed or false alarms, suggesting a need for further research to strengthen the identification of sensitive and non-sensitive areas and improve model prediction accuracy.

[0122] To better demonstrate the effectiveness of the method in the embodiments of this application, the following explanation is based on disaster case testing.

[0123] Case studies of flood disasters:

[0124] Figure 5 This is a schematic diagram illustrating a case study of a flood disaster in location A caused by a rainstorm, provided as an embodiment of this application.

[0125] At approximately 8:40 PM on July 19, 2024, a sudden rainstorm hit area A, triggering a flash flood. From Figure 5 It can be seen that the center of the rainstorm gradually moved towards location A at 3 PM that day, reaching it at 8 PM, with hourly rainfall exceeding 50 mm / h at one point. As the rainfall continued, the area with high flow gradually increased, reaching its maximum at 11 PM. Case studies of the flood in location A show that the model captured the flood event quite well.

[0126] Case studies of flash floods and debris flows:

[0127] Figure 6 This is a schematic diagram illustrating a case study of a flash flood and debris flow disaster in location B caused by a rainstorm, as provided in an embodiment of this application.

[0128] At approximately 2:30 a.m. on July 20, 2024, a flash flood and mudslide disaster occurred in area B due to torrential rain. Figure 6 The model was used to analyze regional precipitation and predicted landslide risks during this period. Results showed that rainfall peaked at 2:00 AM, then gradually decreased and ceased. During this process, as rainfall continued, multiple landslide risks were predicted, with high-risk areas covering almost the entire region, consistent with actual reported disaster conditions. This indicates that the model was relatively accurate in identifying landslide locations and successfully captured this disaster event.

[0129] Case studies of floods and geological disasters:

[0130] Figure 7 This is a schematic diagram illustrating a case study of flooding and geological disasters in location C caused by a rainstorm, provided as an embodiment of this application.

[0131] In the early morning of August 3, 2024, multiple flash floods occurred in area C, causing geological disasters such as landslides and mudslides. Figure 7 The distribution of flood flow and landslide risk in area C during this period shows that the forecast flood flow in the affected area exceeds 4000 m³ / h. 3 The floodwaters in the area were still rising at 5:00 AM on the 3rd, and the area was located in a high-risk landslide zone, making it highly susceptible to secondary disasters such as mudslides. Case studies of floods and geological disasters in area C showed that the model captured the disaster process. This also indicates that floods and landslides develop in tandem, making coupled forecasting of floods and landslides essential.

[0132] The method in this application innovatively combines four influencing factors—rainfall intensity, land cover type, soil type, and slope—to dynamically identify landslide-prone areas. Different landslide risk thresholds and risk level ranges are set for different prone areas, thereby improving the accuracy of the model's regional calculation results. Results show that in long-sequence tests, the AUC value for flood forecasting exceeds 0.8, and the accuracy for landslide forecasting exceeds 0.7, demonstrating the reliability of the model's coupled forecasts of floods and landslides. In case studies, the model successfully captured the occurrence and development processes of floods, flash floods, debris flows, and landslides, providing guidance for disaster early warning and forecasting to some extent. The results analysis also shows that the model exhibits false negatives and false negatives in some study areas, indicating that these areas could be prioritized for identification and preprocessing in future studies to ensure consistency with reality.

[0133] Based on the description of the foregoing method embodiments, in one embodiment of this application, a dynamic identification device for landslide-prone areas is also proposed. Please refer to [link to relevant documentation]. Figure 8 , Figure 8 This is a schematic diagram of the structure of a dynamic identification device for landslide-prone areas provided in an embodiment of this application. Figure 8 As shown, the dynamic identification device for landslide-prone areas includes:

[0134] Module 810 is used to construct a hydrological model and perform hydrological process simulation based on the hydrological model.

[0135] The construction module 810 is also used to construct a slope stability model based on the hydrological process;

[0136] The construction module 810 is also used to extend the slope stability model to a regional scale and couple it with the hydrological model to obtain a hydrological-landslide coupled model.

[0137] The identification module 820 is used to dynamically identify landslide-prone areas based on the hydrological-landslide coupling model and in combination with landslide influencing factors, and obtain identification results. The landslide influencing factors include, but are not limited to, rainfall intensity, slope, land cover and soil type.

[0138] Based on the description of the foregoing method embodiments, an electronic device is also proposed in one embodiment of this application. Please refer to... Figure 9 , Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 9 As shown, the electronic device 900 includes a processor 901 and a memory 902. The memory 902 stores a computer program, which, when executed by the processor 901, will perform actions such as... Figure 1 Any step in the method embodiment shown. The electronic device 900 may also include input / output devices, etc. In a specific embodiment, the electronic device may be a terminal device, etc.

[0139] In one embodiment, a computer-readable storage medium is also provided, which stores a computer program that, when executed by a processor 901, causes the processor 901 to perform any of the steps in the above method embodiments.

[0140] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0141] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0142] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for dynamic identification of landslide-prone areas based on a hydrological-landslide coupling model, characterized in that, The method includes: Construct a hydrological model and simulate hydrological processes based on the hydrological model; A slope stability model is constructed based on the aforementioned hydrological process; The slope stability model is extended to a regional scale and coupled with the hydrological model to obtain a hydrological-landslide coupled model. Based on the aforementioned hydrological-landslide coupling model, combined with landslide influencing factors, landslide-prone areas are dynamically identified, and identification results are obtained. The landslide influencing factors include, but are not limited to, rainfall intensity, slope, land cover, and soil type.

2. The method for dynamic identification of landslide-prone areas based on a hydrological-landslide coupling model according to claim 1, characterized in that, The construction of the hydrological model includes: The infiltration capacity of the remaining precipitation after it is intercepted by the vegetation canopy is analyzed to determine whether there is excessive infiltration runoff. In addition to the excessive infiltration runoff, the precipitation that infiltrates into the soil is divided into runoffs according to the three-layer soil generalization model, taking into account soil evapotranspiration. For each grid, the precipitation is divided according to the infiltration capacity to determine the infiltration capacity curve of the grid. The expression for the infiltration volume is determined based on the infiltration capacity curve.

3. The method for dynamic identification of landslide-prone areas based on a hydrological-landslide coupling model according to claim 2, characterized in that, The hydrological process simulation based on the hydrological model includes: Based on the aforementioned infiltration volume expression, a linear reservoir is used to simulate the confluence process of surface runoff and groundwater runoff. At each time step, the runoff generation of net surface rainfall and infiltration rainfall is calculated, and then the confluence time of the runoff generation on the upstream grid to the downstream grid is calculated, and its impact on the runoff generation and runoff of the downstream grid is analyzed.

4. The method for dynamic identification of landslide-prone areas based on a hydrological-landslide coupling model according to claim 3, characterized in that, The construction of the slope stability model based on the hydrological process includes: Establish the expression for the shear stress of a potential landslide at the sliding surface; Based on the aforementioned shear stress expression, the force equilibrium relationship of the slope is determined using the ultimate slope stability theory. Based on the force balance relationship of the slope and the description of soil infiltration and water content in the hydrological model, the expression for the slope safety factor is obtained.

5. The method for dynamic identification of landslide-prone areas based on a hydrological-landslide coupling model according to claim 4, characterized in that, The dynamic identification of landslide-prone areas based on the hydrological-landslide coupling model, combined with landslide influencing factors, includes: All hydrological variables and fluxes are calculated using the hydrological model in the hydrological-landslide coupled model. The slope safety factor is calculated using the landslide model in the hydrology-landslide coupling model, the hydrological variables and fluxes, and the initial conditions. The landslide disaster occurrence rate of each grid is calculated using the landslide disaster occurrence rate expression, which is based on the landslide influencing factor. Based on the landslide disaster incidence rate of each grid, the slope safety factor, the safe area and the landslide-prone area, the landslide occurrence threshold and risk level range of each grid are determined.

6. The method for dynamic identification of landslide-prone areas based on a hydrological-landslide coupling model according to claim 5, characterized in that, The expression for the landslide disaster incidence rate is: Among them, R j k represents the incidence rate of the k-th type of disaster under the given j-th type of variable; K represents the disaster type, where 0 represents a rainfall event that did not cause a landslide disaster, and 1 represents a landslide disaster event caused by rainfall; J represents the category of the variable, including the rainfall intensity, the slope, the surface cover, and the soil type; n(J = jandK = k) represents the number of landslide disaster events caused by rainfall when J belongs to the j-th category; n[J = j and(K = k or K = 0)] represents the number of rainfall events when J belongs to the j-th category.

7. The method for dynamic identification of landslide-prone areas based on a hydrological-landslide coupling model according to claim 1, characterized in that, The identification results include forecast results and accuracy verification results; the forecast results include a landslide risk distribution map of a specific area, and the accuracy verification results include an ROC curve, which is used to evaluate the predictive performance of the model in different months.

8. A dynamic identification device for landslide-prone areas, characterized in that, include: A construction module is used to build a hydrological model and simulate hydrological processes based on the hydrological model. The construction module is also used to construct a slope stability model based on the hydrological process; The construction module is also used to extend the slope stability model to a regional scale and couple it with the hydrological model to obtain a hydrological-landslide coupled model. The identification module is used to dynamically identify landslide-prone areas based on the hydrological-landslide coupling model and in combination with landslide influencing factors, and obtain identification results. The landslide influencing factors include, but are not limited to, rainfall intensity, slope, land cover and soil type.

9. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, the processor performs the steps of the method as described in any one of claims 1-7.

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