Drainage basin level activity landslide river blocking probability evaluation method, device, medium and product

Abstract

The invention discloses a drainage basin level activity landslide river plugging probability evaluation method and device, a medium and a product, and relates to the field of geological disaster recognition and risk evaluation.The method comprises the steps that a historical landslide river plugging distribution area with the same landform as a target area is obtained; establishing a landslide river-plugging database according to the historical landslide river-plugging samples in the historical landslide river-plugging distribution area and the potential landslide river-plugging samples in the target area; performing correlation analysis on historical landslide river plugging samples in the landslide river plugging database, and determining index variables; establishing a landslide river blocking probability discrimination model according to the index variables; and obtaining the index variable of the target area, and determining the drainage basin level potential landslide river plugging probability of the target area by adopting the landslide river plugging probability discrimination model. According to the method, the accuracy of judging the river blocking probability of the river-basin-level movable landslide can be improved, and then the pertinence, scientificity and accuracy of graded management and control of the river blocking of the potential landslide are improved.

Description

Technical Field

[0001] The present application relates to the field of geological disaster identification and risk assessment, and particularly to a method, device, medium and product for evaluating the probability of river blocking by active landslides at the basin level. Background Art

[0002] At present, the research results on river blocking by landslides mainly focus on the identification and monitoring of typical river-blocking landslides, the characteristics and stability evaluation of landslide dams, the prediction and analysis of river blocking - breaching, the emergency monitoring and treatment of disaster situations, etc. There is a lack of research on a rapid evaluation method for the possibility of potential river blocking by landslides at the basin level. The river-blocking discrimination model has poor specificity and relatively rough classification.

[0003] Therefore, carrying out research on a rapid evaluation method for the possibility of river blocking by landslides can provide technical support for reasonably selecting landslides with a high probability of river blocking for further investigation and evaluation work, improve the pertinence, scientificity and accuracy of hierarchical control of potential river-blocking landslides, reduce the harm caused by landslide dams and the subsequent flood after breaching, and effectively reduce the expenditure on disaster prevention and mitigation related work. Summary of the Invention

[0004] The purpose of the present application is to provide a method, device, medium and product for evaluating the probability of river blocking by active landslides at the basin level, which can improve the accuracy of discriminating river blocking by active landslides at the basin level, and further improve the pertinence, scientificity and accuracy of hierarchical control of potential river-blocking landslides.

[0005] To achieve the above purpose, the present application provides the following solutions: In the first aspect, the present application provides a method for evaluating the probability of river blocking by active landslides at the basin level, and the method for evaluating the probability of river blocking by active landslides at the basin level includes: Obtain a historical river-blocking landslide distribution area with the same geomorphic form as the target area; the geomorphic form includes: altitude and relative height; Establish a river-blocking landslide database based on historical river-blocking landslide samples in the historical river-blocking landslide distribution area and potential river-blocking landslide samples in the target area; Conduct a correlation analysis on the historical river-blocking landslide samples in the river-blocking landslide database to determine index variables; Establish a river-blocking landslide probability discrimination model based on the index variables; Obtain the index variables of the target area, and use the river-blocking landslide probability discrimination model to determine the probability of potential river blocking by landslides at the basin level in the target area.

[0006] Optionally, the step of establishing a river-blocking landslide database based on historical river-blocking landslide samples in the historical river-blocking landslide distribution area and potential river-blocking landslide samples in the target area specifically includes: Obtain historical landslide-dammed river samples and potential landslide-dammed river samples in the target area based on existing research data and multi-source remote sensing data; the existing research data includes: published papers, monographs, and 1:200,000 public regional geological maps; the multi-source remote sensing data includes: digital elevation model data, radar satellite data, and high-resolution optical satellite data; Establish a landslide-dammed river database based on historical landslide-dammed river samples and potential landslide-dammed river samples in the target area; the categories of the landslide-dammed river database include: landslide elements, geological environment background, river channel conditions, and barrier dam elements; the landslide elements include: longitudinal length of the landslide mass, transverse width of the landslide mass, planar area of the landslide, landslide volume, and elevation difference between the front and rear edges of the landslide; the geological environment background includes: slope gradient and distance from the fault; the river channel conditions include: river channel width and water depth; the barrier dam elements include: transverse width of the barrier dam, height of the barrier dam, and degree of river damming.

[0007] Optionally, perform a correlation analysis on the historical landslide-dammed river samples in the landslide-dammed river database to determine the index variables, specifically including: Determine the variance inflation factor values between the parameters in different categories in the landslide-dammed river database respectively; Perform a correlation analysis on the parameters without multicollinearity to determine the correlation; the parameters without multicollinearity are the parameters with a variance inflation factor value < 10; Take the parameters with a correlation greater than or equal to the correlation threshold as the index variables.

[0008] Optionally, the correlation analysis includes: Pearson correlation coefficient method, Spearman rank correlation coefficient method, Kendall rank correlation coefficient method, chi-square test method, and covariance method.

[0009] Optionally, establish a landslide-dammed river probability discrimination model based on the index variables, specifically including: Use the formula Establish a landslide-dammed river probability discrimination model; where, is the landslide-dammed river probability, are all index variables, is a constant, A6 is the slope gradient, and e is the natural logarithm.

[0010] Optionally, obtain the index variables of the target area and use the landslide-dammed river probability discrimination model to determine the potential landslide-dammed river probability at the basin level in the target area, specifically including: Judge whether the index variables of the target area meet the potential landslide-dammed river conditions; the potential landslide-dammed river conditions are that the elevation difference between the front and rear edges of the landslide > 500m, the landslide volume > 1×10 7 m 3 ³, the slope gradient > 35°, and the distance from the fault < 1km; If it is satisfied, the probability discrimination model for landslide-dammed river is used to determine the potential landslide-dammed river probability P at the basin level in the target area; Evaluation is carried out according to the potential landslide-dammed river probability at the basin level in the target area; when the evaluation result is P < 0.5, the target area is for partial river damming or no river damming; when the evaluation result is 0.5 ≤ P < 0.7, the possibility of complete river damming in the target area is low; when the evaluation result is 0.7 ≤ P < 0.8, the possibility of complete river damming in the target area is medium; when the evaluation result is P ≥ 0.8, the possibility of complete river damming in the target area is high.

[0011] In a second aspect, the present application provides an evaluation device for the probability of active landslide-dammed river at the basin level, and the evaluation device for the probability of active landslide-dammed river at the basin level includes: A data acquisition module, configured to acquire a historical landslide-dammed river distribution area having the same geomorphic form as the target area; the geomorphic form includes: altitude and relative height; A database establishment module, configured to establish a landslide-dammed river database according to historical landslide-dammed river samples in the historical landslide-dammed river distribution area and potential landslide-dammed river samples in the target area; An index variable determination module, configured to perform a correlation analysis on historical landslide-dammed river samples in the landslide-dammed river database to determine index variables; A discrimination model establishment module, configured to establish a landslide-dammed river probability discrimination model according to the index variables; An evaluation module, configured to acquire the index variables of the target area and use the landslide-dammed river probability discrimination model to determine the potential landslide-dammed river probability at the basin level in the target area.

[0012] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the method for evaluating the probability of active landslide-dammed river at the basin level.

[0013] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for evaluating the probability of active landslide-dammed river at the basin level is implemented.

[0014] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, the method for evaluating the probability of active landslide-dammed river at the basin level is implemented.

[0015] According to the specific embodiments provided by the present application, the present application has the following technical effects: The present application provides a method, device, medium and product for evaluating the probability of active landslide damming at the basin level. By establishing a landslide damming database based on historical landslide damming samples in the historical landslide damming distribution area and potential landslide damming samples in the target area; and selecting index variables closely related to damming according to the landslide damming database; fitting a landslide damming probability discrimination model suitable for the target area according to the index variables, the problems of poor specificity and relatively rough classification in the existing damming discrimination model are improved. The present application can provide technical support for reasonably selecting landslides with a high probability of significant damming for further investigation and evaluation work, improve the pertinence, scientificity and accuracy of hierarchical control of potential landslide damming, reduce the hazards caused by landslide dams and the floods after their breach, and effectively reduce the expenditure of funds for disaster prevention and mitigation related work. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0017] Figure 1 Schematic flow chart of a method for evaluating the probability of active landslide damming at the basin level in an embodiment of the present application; Figure 2 Remote sensing image of an active landslide and interferometric synthetic aperture radar (InSAR) line-of-sight (LOS) deformation rate map in an embodiment of the present application; Figure 3 Schematic diagram of each element of landslide damming in an embodiment of the present application; Figure 4 Correlation heat map in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0019] To make the above objects, features and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0020] In an exemplary embodiment, as Figure 1 shown, a method for evaluating the probability of river blocking by active landslides at the basin level is provided. The method includes the following S101 to S105. Among them: S101, obtaining the historical landslide river-blocking distribution area with the same geomorphic form as the target area; the geomorphic form includes: altitude and relative height; The above steps follow the principle of intra-regional similarity. Based on two geomorphic indicators, altitude and relative height, the historical landslide river-blocking distribution area with the same geomorphic form as the target area is selected.

[0021] The geomorphic form can be divided into 7 types: plain, hill, low mountain, middle mountain, high mountain, extremely high mountain, and plateau, as shown in Table 1.

[0022] Table 1

[0023] S102, establishing a landslide river-blocking database according to the historical landslide river-blocking samples in the historical landslide river-blocking distribution area and the potential landslide river-blocking samples in the target area; S102 obtains the historical landslide river-blocking samples and the potential landslide river-blocking samples in the target area according to the existing research materials and multi-source remote sensing data; the existing research materials include: published papers, monographs, 1:200,000 public version regional geological maps, and basin-level hydrological monitoring data; the multi-source remote sensing data includes: ALOS satellite 12.5m Digital Elevation Model (DEM) data, Sentinel-1 radar satellite data, and high-resolution optical satellite data; the historical landslide river-blocking samples are obtained through published papers and monographs, and the potential landslide river-blocking samples are obtained through high-resolution optical satellite images ( Figure 2 part (a)), synthetic aperture radar interferometry line-of-sight direction deformation rate ( Figure 2 part (b)), and other multi-source remote sensing technologies to delineate the range of active landslides with deformation signs.

[0024] Establish a landslide river-blocking database according to the historical landslide river-blocking samples and the potential landslide river-blocking samples in the target area; the categories of the landslide river-blocking database include: landslide elements, geological environment background, river channel conditions, and barrier lake dam elements; the landslide elements include but are not limited to: longitudinal length A1 of the sliding mass, transverse width A2 of the sliding mass, planar area A3 of the landslide, volume A4 of the landslide, and height difference A5 between the front and rear edges of the landslide; the geological environment background includes but is not limited to: slope gradient A6 and distance A7 from the fault; the river channel conditions include but are not limited to: river channel width A8 and water depth A9; the barrier lake dam elements include but are not limited to: transverse river width A10 of the barrier lake dam, height A11 of the barrier lake dam, and river-blocking degree A12.

[0025] Combined with existing research data, based on the ArcGIS software, the elevation difference A5 between the front and rear edges of the landslide and the slope gradient A6 are obtained from DEM data. The longitudinal length A1 of the landslide mass, the transverse width A2 of the landslide mass, the planar area A3 of the landslide, the river width A8, the transverse width A10 of the historical landslide-dammed river barrier across the river, and the height A11 of the historical landslide-dammed river barrier are obtained from the GF-2 optical satellite images. The landslide volume A4 in the high mountain area is calculated according to the relational expression as shown in part (a) of Figure 3 . The distance A7 from the fault is measured with reference to the 1:200,000 open version regional geological map. As shown in part (b) of Figure 3 and part (c) of Figure 3 , the river damming degree A12 is determined according to the difference D between the transverse width A10 of the barrier across the river and the river width A8. When D≥0, it means complete river damming, and the value is 1; when D<0, it means partial river damming, and the value is 0. The river depth A9 is obtained from the basin-level hydrological monitoring data.

[0026] S103. Perform a correlation analysis on the historical landslide-dammed river samples in the landslide-dammed river database to determine the index variables. The correlation analysis includes: Pearson correlation coefficient method, Spearman rank correlation coefficient method, Kendall rank correlation coefficient method, chi-square test method, and covariance method.

[0027] S103 specifically includes: Respectively determine the variance inflation factor (VIF) values between the parameters in different categories of landslide elements, geological environment background, river channel conditions, and barrier elements in the landslide-dammed river database; Perform a correlation analysis on the parameters without multicollinearity to determine the correlation r. The parameters without multicollinearity are the parameters with a variance inflation factor value<10; Take the parameters with a correlation r greater than or equal to the correlation threshold as the index variables (x1, x2, x3, …, xn). The correlation threshold is 0.5.

[0028] In a specific embodiment of the present application, taking the high mountainous areas through which the main streams of Jinsha River, Yalong River, Dadu River, and Minjiang River flow as the test areas, the variance inflation factor (VIF) values between the parameters of various categories such as historical landslide dammed river elements, geological environment background, river channel conditions, and barrier dam elements are calculated respectively. For the landslide elements, the VIF values of the landslide volume A4 and the longitudinal length A1 of the landslide mass, the transverse width A2 of the landslide mass, and the planar area A3 of the landslide are > 10. For the river channel conditions, the VIF values of the river channel width A8 and the river depth A9 are > 10. For the barrier dam elements, the VIF values of the transverse river width A10 of the barrier dam, the height A11 of the barrier dam, and the degree of river damming A12 are > 10, indicating the existence of multicollinearity. After removing these parameters, the Spearman rank correlation coefficient method is used to calculate the correlation r of the landslide volume A4, the height difference A5 between the front and rear edges of the landslide, the slope gradient A6, the distance A7 from the fault, the river channel width A8, and the transverse river width A10 of the barrier dam. The parameters with a correlation r ≥ 0.5 with the transverse river width A10 of the barrier dam are selected as the river damming index variables, as Figure 4 shown, it is determined that the height difference A5 between the front and rear edges of the landslide, the landslide volume A4, and the river channel width A8 are the index variables closely related to the river damming in the target area; S104. Establish a landslide dammed river probability discrimination model based on the index variables; among them, conduct a Logistic regression analysis of the index variables, fit and optimize the landslide dammed river probability discrimination model; S104 specifically includes: Use the formula to establish a landslide dammed river probability discrimination model; where is the landslide dammed river probability, is a constant, A6 is the slope gradient, and e is the natural logarithm.

[0029] Specifically, using Matlab software, based on the three parameters of the height difference A5 between the front and rear edges of the landslide, the landslide volume A4, and the river channel width A8, conduct a Logistic regression analysis, and optimize the model based on the Akaike information criterion (AIC). Fit the model as: AIC = 120.6. In the formula, P is the probability value, taking values from 0 to 1. If it is greater than 0.5, it means complete blockage; otherwise, it means partial blockage or no blockage. Use 41 historical landslide dammed rivers in a certain area to verify the proposed discrimination model. Existing research shows that all 41 historical landslide dammed rivers have caused complete river damming in the geological history period. Using the fitted river damming probability discrimination model this time, for the 41 historical landslide dammed rivers, the P value ≥ 0.8, and it is determined as complete river damming, which is consistent with the actual situation, indicating that the model has a high fitting degree and has practical application significance.

[0030] S105. Obtain the index variables of the target area, and use the landslide dammed river probability discrimination model to determine the potential landslide dammed river probability at the basin level of the target area.

[0031] S105 specifically includes: Judge whether the index variable of the target area meets the conditions of potential landslide damming of the river; the potential landslide damming conditions are that the height difference between the front and rear edges of the landslide > 500m, the volume of the landslide > 1×10 7 m 3 , the slope gradient > 35° and the distance from the fault < 1km; If it is satisfied, use the landslide damming probability discrimination model to determine the potential landslide damming probability P at the basin level of the target area; Evaluate according to the potential landslide damming probability at the basin level of the target area; when the evaluation result is P < 0.5, the target area is locally blocked or not blocked; when the evaluation result is 0.5 ≤ P < 0.7, the possibility of complete damming in the target area is low; when the evaluation result is 0.7 ≤ P < 0.8, the possibility of complete damming in the target area is medium; when the evaluation result is P ≥ 0.8, the possibility of complete damming in the target area is high.

[0032] Based on multi-source remote sensing technology, a total of 31 potentially landslide-dammed rivers that are deforming have been identified in the Batang-Baiyu section of the upper reaches of the Jinsha River. The results show that the potential landslide damming of the river in the Batang-Baiyu section of the upper reaches of the Jinsha River has all passed the determination of landslide damming conditions, with 3 having a low possibility of complete damming, 5 having a medium possibility of complete damming, and 23 having a high possibility of complete damming.

[0033] Based on the same inventive concept, the embodiment of the present application also provides a basin-level active landslide damming probability evaluation device for implementing the above-mentioned basin-level active landslide damming probability evaluation method. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the basin-level active landslide damming probability evaluation device provided below can refer to the limitations on the basin-level active landslide damming probability evaluation method in the above text, and will not be elaborated here.

[0034] In an exemplary embodiment, a basin-level active landslide damming probability evaluation device is provided, including: A data acquisition module for acquiring a historical landslide damming distribution area with the same geomorphic form as the target area; the geomorphic form includes: altitude and relative height; A database establishment module for establishing a landslide damming database according to the historical landslide damming samples in the historical landslide damming distribution area and the potential landslide damming samples in the target area; An index variable determination module for performing correlation analysis on the historical landslide damming samples in the landslide damming database to determine the index variables; A discrimination model establishment module for establishing a landslide damming probability discrimination model according to the index variables; An evaluation module for acquiring the index variables of the target area and using the landslide damming probability discrimination model to determine the potential landslide damming probability at the basin level of the target area.

[0035] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for evaluating the probability of a landslide blocking a river at the watershed level.

[0036] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0037] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0038] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0039] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0040] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0041] In the present application, all actions of obtaining signals, information, or data are carried out on the premise of complying with the corresponding data protection regulations and policies of the country where the location is located and obtaining the authorization given by the owner of the corresponding device.

[0042] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope described in this specification.

[0043] In this text, specific examples are used to illustrate the principle and implementation of this application. The description of the above embodiments is only for helping to understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation and application scope. To sum up, the content of this specification should not be construed as a limitation to this application.

Claims

1. A method for evaluating the probability of river blocking by active landslide at the basin level, characterized in that: The basin-level active landslide blocking river probability assessment method includes: Obtain the historical landslide river blocking distribution area with the same landform as the target area; the landform includes: altitude and relative height; A landslide river blocking database is established based on historical landslide river blocking samples in the historical landslide river blocking distribution area and potential landslide river blocking samples in the target area; Conduct correlation analysis on historical landslide-river blocking samples in the landslide-river blocking database to determine indicator variables; According to the indicator variables, a model for distinguishing the probability of landslide blocking the river was established; The indicator variables of the target area are obtained, and the probability discrimination model of landslide blocking the river is used to determine the probability of potential landslide blocking the river at the basin level in the target area.

2. The method for evaluating the probability of river blocking caused by active landslide at the watershed level according to claim 1 is characterized in that: The landslide river blocking database is established based on the historical landslide river blocking samples in the historical landslide river blocking distribution area and the potential landslide river blocking samples in the target area, specifically including: Obtain historical landslide river blocking samples and potential landslide river blocking samples in the target area based on existing research data and multi-source remote sensing data; the existing research data include: publicly published papers, monographs and 1:200,000 public regional geological maps; the multi-source remote sensing data include: digital elevation model data, radar satellite data and high-resolution optical satellite data; A landslide river blocking database is established based on historical landslide river blocking samples and potential landslide river blocking samples in the target area; the categories of the landslide river blocking database include: landslide elements, geological environment background, river channel conditions and dam elements; the landslide elements include: the longitudinal length of the sliding body, the lateral width of the sliding body, the plane area of ​​the landslide, the volume of the landslide and the height difference between the front and rear edges of the landslide; the geological environment background includes: the slope gradient and the distance from the fault; the river channel conditions include: the river channel width and the river water depth; the dam elements include: the dam lateral river width, the dam height and the degree of river blocking.

3. The method for evaluating the probability of river blocking caused by active landslide at the watershed level according to claim 2 is characterized in that: The correlation analysis of the historical landslide river blocking samples in the landslide river blocking database is performed to determine the indicator variables, specifically including: Determine the variance inflation factor values ​​between parameters in different categories in the landslide river blocking database; Correlation analysis was performed on the parameters without multicollinearity to determine the correlation; the parameters without multicollinearity were those with variance inflation factor values ​​< 10; Parameters with correlations greater than or equal to the correlation threshold are taken as indicator variables.

4. The method for evaluating the probability of river blocking caused by active landslide at the watershed level according to claim 1 is characterized in that: Correlation analysis included: Pearson correlation coefficient method, Spearman rank correlation coefficient method, Kendall rank correlation coefficient method, chi-square test method and covariance method.

5. The method for evaluating the probability of river blocking caused by active landslide at the watershed level according to claim 1 is characterized in that: The landslide-blocking river probability discrimination model is established according to the indicator variables, specifically including: Using the formula Establish a model for determining the probability of landslide blocking a river; in, The probability of landslide blocking the river, are indicator variables, is a constant, A6 is the slope of the ramp, and e is the natural logarithm.

6. The method for evaluating the probability of river blocking caused by active landslide at the watershed level according to claim 1 is characterized in that: The method of obtaining the indicator variables of the target area and using the landslide blocking river probability discrimination model to determine the basin-level potential landslide blocking river probability of the target area specifically includes: Determine whether the indicator variables of the target area meet the potential landslide blocking river conditions; the potential landslide blocking river conditions are that the height difference between the front and rear edges of the landslide is greater than 500m, and the landslide volume is greater than 1×10 7 m 3 , slope gradient > 35° and distance from the fault < 1 km; If it is satisfied, the probability of landslide blocking the river is determined by using the landslide blocking river probability discrimination model at the target area basin level; An evaluation is conducted based on the probability of potential landslide blocking the river at the basin level in the target area; when the evaluation result is P<0.5, the target area is partially blocked or not blocked; when the evaluation result is 0.5≤P<0.7, the possibility of complete blockage of the river in the target area is low; when the evaluation result is 0.7≤P<0.8, the possibility of complete blockage of the river in the target area is medium; when the evaluation result is P≥0.8, the possibility of complete blockage of the river in the target area is high.

7. A basin-level active landslide blocking river probability assessment device, characterized in that: The basin-level active landslide blocking river probability assessment equipment includes: A data acquisition module is used to acquire the historical landslide-blocked river distribution area with the same landform as the target area; the landform includes: altitude and relative height; A database establishment module is used to establish a landslide river blocking database based on historical landslide river blocking samples in the historical landslide river blocking distribution area and potential landslide river blocking samples in the target area; An indicator variable determination module is used to perform correlation analysis on historical landslide river blocking samples in the landslide river blocking database to determine the indicator variables; The discriminant model building module is used to build a landslide-blocking river probability discriminant model based on indicator variables; The evaluation module is used to obtain the indicator variables of the target area and use the landslide river blocking probability discrimination model to determine the potential landslide river blocking probability at the watershed level in the target area.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the basin-level active landslide river blocking probability assessment method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating the probability of river blocking by active landslide at a watershed level described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for evaluating the probability of river blocking by active landslide at a watershed level described in any one of claims 1 to 6 is implemented.

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