Landslide geological disaster evaluation method and system, electronic equipment and storage medium

Abstract

The invention relates to the technical field of geological disasters, and particularly discloses a landslide geological disaster evaluation method and system, electronic equipment and a storage medium, and the method comprises the steps: determining a plurality of target evaluation factors related to the landslide geological disasters of a target region, and carrying out the evaluation of the landslide geological disasters based on all target evaluation factors; constructing a landslide geological disaster evaluation model of the target area and training the landslide geological disaster evaluation model; and inputting the current geological environment data of the target area into a trained landslide geological disaster evaluation model to obtain a landslide geological disaster evaluation result of the target area. The method can accurately predict the landslide geological disaster, reduces the loss caused by the landslide disaster as much as possible, and provides guidance for making a landslide disaster prevention and control plan and improving the landslide disaster prevention and control capability.

Description

Background Art

[0002] Since the 21st century, with the enhancement of my country's comprehensive strength, the continuous growth of population and the gradual improvement of the national economic level, the original ecosystem of nature has been destroyed, resulting in an increase in the frequency of a series of geological disasters such as landslides, collapses, mud-rock flows, ground fissures, ground collapse, ground subsidence, unstable slopes, etc. Among the geological disasters that occur in my country, landslide geological disasters are the most numerous. There are many factors that induce landslides, which can be mainly divided into internal factors and external factors. Among them, internal factors (such as topography, geomorphology, hydrology, strata, lithology, etc.) play a decisive role in the occurrence of landslide disasters, and external factors (such as human engineering activities, rainfall, earthquakes, etc.) constitute the triggering factors for the occurrence of landslide disasters. These geological disasters greatly threaten the lives and property safety of the general public.

[0003] Therefore, it is urgent to provide a technical solution to solve the above problems. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a landslide geological disaster assessment method, system, electronic equipment and storage medium.

[0005] In a first aspect, the present invention provides a landslide geological disaster assessment method, the technical solution of the method is as follows:

[0006] Determine a plurality of target evaluation factors associated with landslide geological hazards in a target area, and construct and train a landslide geological hazard evaluation model for the target area based on all target evaluation factors;

[0007] The current geological environment data of the target area is input into the trained landslide geological hazard assessment model to obtain the landslide geological hazard assessment result of the target area.

[0008] The beneficial effects of a landslide geological disaster assessment method of the present invention are as follows:

[0009] The method of the present invention can accurately predict landslide geological disasters, reduce the losses caused by landslide disasters as much as possible, and provide guidance for formulating landslide disaster prevention and control plans and improving landslide disaster prevention and control capabilities.

[0010] On the basis of the above scheme, the landslide geological disaster assessment method of the present invention can also be improved as follows.

[0011] In an optional manner, the step of determining a plurality of target evaluation factors associated with landslide geological hazards in a target area includes:

[0012] A plurality of initial evaluation factors for evaluating landslide geological hazards in the target area are selected, and a plurality of target evaluation factors are screened from all the initial evaluation factors by using the principal component analysis method and combining the specific value of each initial evaluation factor.

[0013] In an optional manner, the step of constructing a landslide geological hazard assessment model for the target area based on all target assessment factors includes:

[0014] The landslide geological hazard evaluation model of the target area is constructed by using the entropy index method and the random forest algorithm and combining the specific value of each target evaluation factor.

[0015] In an optional manner, the method further includes:

[0016] Obtaining the ROC curve of the trained landslide geological hazard assessment model, and judging whether the area between the ROC curve and the horizontal axis of the trained landslide geological hazard assessment model exceeds a preset value, and obtaining a judgment result;

[0017] When the judgment result is yes, it is determined that the accuracy of the trained landslide geological hazard assessment model meets the requirements, and the step of inputting the current environmental data of the target area into the trained landslide geological hazard assessment model to obtain the landslide geological hazard assessment result of the target area is executed.

[0018] In an optional manner, the landslide geological hazard assessment model is a RF-IOE model.

[0019] In a second aspect, the present invention provides a landslide geological disaster assessment system, the technical solution of the system is as follows:

[0020] Includes: processing module and evaluation module;

[0021] The processing module is used to: determine multiple target evaluation factors associated with landslide geological hazards in the target area, and based on all the target evaluation factors, construct and train a landslide geological hazard evaluation model for the target area;

[0022] The evaluation module is used to: input the current geological environment data of the target area into the trained landslide geological disaster evaluation model to obtain the landslide geological disaster evaluation result of the target area.

[0023] The beneficial effects of a landslide geological disaster assessment system of the present invention are as follows:

[0024] The system of the present invention can accurately predict landslide geological disasters, reduce the losses caused by landslide disasters as much as possible, and provide guidance for formulating landslide disaster prevention and control plans and improving landslide disaster prevention and control capabilities.

[0025] On the basis of the above scheme, the landslide geological disaster assessment system of the present invention can also be improved as follows.

[0026] In an optional manner, the processing module is specifically used to:

[0027] A plurality of initial evaluation factors for evaluating landslide geological hazards in the target area are selected, and a plurality of target evaluation factors are screened from all the initial evaluation factors by using the principal component analysis method and combining the specific value of each initial evaluation factor.

[0028] In an optional manner, the processing module is specifically used to:

[0029] The landslide geological hazard evaluation model of the target area is constructed by using the entropy index method and the random forest algorithm and combining the specific value of each target evaluation factor.

[0030] In a third aspect, a technical solution of an electronic device of the present invention is as follows:

[0031] The method comprises a memory, a processor and a program stored in the memory and running on the processor. When the processor executes the program, the steps of the landslide geological disaster assessment method of the present invention are implemented.

[0032] In a fourth aspect, the present invention provides a computer-readable storage medium having the following technical solution:

[0033] Instructions are stored in the computer-readable storage medium. When the computer-readable storage medium reads the instructions, the computer-readable storage medium executes the steps of the landslide geological hazard assessment method of the present invention.

[0034] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present invention. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings. In the accompanying drawings:

[0036] Figure 1 A schematic diagram of a flow chart of an embodiment of a landslide geological disaster assessment method of the present invention;

[0037] Figure 2 This is a schematic diagram of landslide susceptibility zones;

[0038] Figure 3A schematic diagram of the structure of an embodiment of a landslide geological disaster assessment system of the present invention;

[0039] Figure 4 The figure is a schematic structural diagram of an embodiment of an electronic device of the present invention. DETAILED DESCRIPTION

[0040] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0041] Figure 1 A flow chart of an embodiment of a landslide geological hazard assessment method provided by the present invention is shown, and the landslide geological hazard assessment method can be executed by an electronic device such as a terminal device or a server. The terminal device can be any fixed or mobile terminal such as a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be a single server or a server cluster composed of multiple servers. Any electronic device can implement the landslide geological hazard assessment method by calling computer-readable instructions stored in a memory through a processor. Figure 1 As shown, the following steps are included:

[0042] S1. Determine a plurality of target evaluation factors associated with landslide geological hazards in a target area, and construct and train a landslide geological hazard evaluation model for the target area based on all target evaluation factors.

[0043] The target area is the area where landslide geological disaster assessment is required in this embodiment, and the selection of the target area can be determined according to the actual situation, and there is no limitation here. The landslide geological disaster assessment model is the RF-IOE model.

[0044] In S1, specifically:

[0045] 1) The step of determining a plurality of target evaluation factors associated with landslide geological hazards in the target area comprises:

[0046] A plurality of initial evaluation factors for evaluating landslide geological hazards in the target area are selected, and a plurality of target evaluation factors are screened from all the initial evaluation factors by using the principal component analysis method and combining the specific value of each initial evaluation factor.

[0047] Among them, according to the historical geological environment data of the target area, multiple initial evaluation factors for evaluating landslide geological hazards in the target area are selected. The geological environment data include but are not limited to: topographic geological data, meteorological and hydrological data, geological disaster point data, DEM data, normalized vegetation index (NDVI), rainfall data, population density data, social and economic development status and other results data. Multiple initial evaluation factors include: elevation, slope, slope aspect, terrain undulation, curvature, profile curvature, plane curvature, ground roughness, terrain wetness index (TWI), average annual precipitation, average precipitation from May to September, land use type, normalized vegetation index (NDVI), residential density, stratum lithology, distance from river, distance from road, distance from fault, elevation variation coefficient, surface cutting depth, a total of 20.

[0048] It should be noted that the occurrence of landslide geological disasters is the result of the combined action of multiple factors. The initial evaluation factors selected in this embodiment may have a certain degree of correlation, which will cause redundancy in the analysis data, increase the complexity of the problem analysis, and affect the accuracy of the landslide geological disaster evaluation model in the target area. In this embodiment, the principal component analysis method in SPSS software is selected to extract a few target evaluation factors from multiple initial evaluation factors, and make them contain most of the information provided by the original variables, and perform principal component analysis on the selected 20 initial evaluation factors. The principal component analysis method in this embodiment uses the Pearson correlation coefficient method, which is a mathematical and statistical method used to measure the closeness and correlation direction of the correlation between multiple factors. Calculate the Pearson correlation coefficient r, the formula is: n represents the number of samples (specific values ​​of a certain initial evaluation factor), x i and i Indicates the specific values ​​corresponding to the two initial evaluation factors, and Represents the average value of the initial evaluation factor. The value range of r is [-1,1]. The larger its absolute value is, the stronger the correlation between the two initial evaluation factors is. When r>0, it means that the two initial evaluation factors are positively correlated; when r<0, it means that the two initial evaluation factors are negatively correlated. In this embodiment, the target evaluation factor can be determined according to the value of r.

[0049] 2) Based on all target evaluation factors, the step of constructing a landslide geological hazard evaluation model for the target area comprises:

[0050] The landslide geological hazard evaluation model of the target area is constructed by using the entropy index method and the random forest algorithm and combining the specific value of each target evaluation factor.

[0051] Among them, the entropy index method is a binary statistical model based on mathematical statistics. In the evaluation of geological disaster susceptibility, on the one hand, it can be used as raw data to predict the susceptibility of disasters, and on the other hand, it can be coupled with other models (algorithms) and used as input data of the coupling model for prediction. The weight value of the target evaluation factor calculated by the entropy index method can be obtained by the following formula: H imax =log2s, W i =I i ×P i ;FR ij represents the frequency ratio of the i-th target evaluation factor in the j-th interval, b represents the proportion of landslide disaster points in the interval corresponding to the target evaluation factor, a represents the proportion of the area of ​​the interval corresponding to the target evaluation factor, and P ij represents the probability density of the i-th target evaluation factor in the j-th interval, S represents the number of intervals of each target evaluation factor, and H i represents the entropy value of the i-th target evaluation factor, H imax represents the maximum entropy value of the i-th target evaluation factor; I i represents the entropy weight of the i-th target evaluation factor; W i Represents the comprehensive weight value of the i-th target evaluation factor.

[0052] Among them, the random forest algorithm is a highly flexible machine learning method, which mainly uses the bootstrap resampling technology to repeatedly extract N samples with replacement from the training samples to re-form a new training set, use the new training set to train the decision tree, repeat the above steps to generate multiple decision trees, form a random forest, and get the final prediction result through voting. Before using the random forest method for prediction, a certain number of samples need to be selected to train the RF model. The study adopts a method combining positive samples (landslide points) and negative samples (non-landslide points), from which a certain number of samples are selected to construct the training set and the validation set, and then the landslide susceptibility of the entire target area is evaluated.

[0053] Among them, the RF-IOE model is based on the entropy index model. In the low-prone and medium-prone areas of the entropy index model, non-landslide points equal to the number of landslide points are randomly selected. The following principles are followed when selecting: first, the distance between two non-landslide points is greater than 1000m, and second, the distance between non-landslide points and landslide points is greater than 1000m, so as to avoid the distance between sample points being too close and interfering with the accuracy of the model. The selected sample points are extracted based on the ArcGIS value extraction to point tool to extract the disaster point data, which together constitute the sample data of the RF-IOE model. The data is imported into the R language, and 70% of the sample point data are randomly selected as the training set for model training, and the remaining 30% of the sample point data are used as the test set to verify the accuracy of the model. The obtained parameters are input into the R language to construct the RF-IOE model.

[0054] S2. Inputting the current geological environment data of the target area into the trained landslide geological hazard assessment model to obtain the landslide geological hazard assessment result of the target area.

[0055] The data type of the current geological environment data is the same as that of the historical geological environment data, only the data values ​​are different. The landslide geological hazard assessment result is a landslide susceptibility zoning map (such as Figure 2 The extent of landslide geological hazards is determined according to the landslide susceptibility zoning map.

[0056] In an optional manner, the method further includes:

[0057] The ROC curve of the trained landslide geological hazard assessment model is obtained, and it is determined whether the area between the ROC curve and the horizontal axis of the trained landslide geological hazard assessment model exceeds a preset value to obtain a determination result.

[0058] When the judgment result is yes, it is determined that the accuracy of the trained landslide geological hazard assessment model meets the requirements, and the step of inputting the current environmental data of the target area into the trained landslide geological hazard assessment model to obtain the landslide geological hazard assessment result of the target area is executed.

[0059] Among them, the ROC curve of the trained landslide geological disaster evaluation model is drawn based on the SPSS software. The area between the ROC curve and the horizontal axis of the landslide geological disaster evaluation model is the AUC (Area Under Curve) value. The AUC value is used to represent the accuracy of the landslide geological disaster evaluation model, and its value range is [0, 1]. The preset value of this embodiment can be set according to the actual situation, such as 0.5. When AUC < 0.5, it indicates that the selected model has a poor effect and the evaluation result is inaccurate; when 0.5 < AUC < 0.7, it indicates that the selected model has an average effect and the evaluation result is average; when 0.7 < AUC < 0.9, it indicates that the selected model has a good effect and the evaluation result is good; when 0.9 < AUC < 1, it indicates that the selected model has a very good effect and the evaluation result is very good; when AUC = 1, it indicates that the selected model has an excellent effect, but this situation is very rare.

[0060] The technical solution of this embodiment can accurately predict landslide geological disasters, minimize the losses caused by landslide disasters, and provide guidance for formulating landslide disaster prevention and control plans and improving landslide disaster prevention and control capabilities.

[0061] Figure 3 Fig. shows a schematic structural diagram of an embodiment of a landslide geological disaster evaluation system 200 provided by the present invention. As Figure 3 shown, the system 200 includes: a processing module 210 and an evaluation module 220;

[0062] The processing module 210 is configured to: determine a plurality of target evaluation factors associated with the landslide geological disaster in the target area, and construct and train a landslide geological disaster evaluation model for the target area based on all the target evaluation factors;

[0063] The evaluation module 220 is configured to: input the current geological environment data of the target area into the trained landslide geological disaster evaluation model to obtain a landslide geological disaster evaluation result of the target area.

[0064] In an optional manner, the processing module 210 is specifically configured to:

[0065] Select a plurality of initial evaluation factors for evaluating the landslide geological disaster in the target area, and use the principal component analysis method and combine the specific values of each initial evaluation factor to screen out a plurality of target evaluation factors from all the initial evaluation factors.

[0066] In an optional manner, the processing module 210 is specifically configured to:

[0067] Use the entropy index method and the random forest algorithm, and combine the specific values of each target evaluation factor to construct the landslide geological disaster evaluation model for the target area.

[0068] In an optional manner, the method further includes: a verification module; the verification module is used to:

[0069] Obtaining the ROC curve of the trained landslide geological hazard assessment model, and judging whether the area between the ROC curve and the horizontal axis of the trained landslide geological hazard assessment model exceeds a preset value, and obtaining a judgment result;

[0070] When the judgment result is yes, it is determined that the accuracy of the trained landslide geological hazard assessment model meets the requirements, and the step of inputting the current environmental data of the target area into the trained landslide geological hazard assessment model to obtain the landslide geological hazard assessment result of the target area is executed.

[0071] In an optional manner, the landslide geological hazard assessment model is a RF-IOE model.

[0072] The technical solution of this embodiment can accurately predict landslide geological disasters, reduce the losses caused by landslide disasters as much as possible, and provide guidance for formulating landslide disaster prevention and control plans and improving landslide disaster prevention and control capabilities.

[0073] The above-mentioned parameters and steps for each module to implement corresponding functions in the landslide geological hazard evaluation system 200 of this embodiment can refer to the parameters and steps in the above embodiment of the landslide geological hazard evaluation method, and will not be repeated here.

[0074] like Figure 4 As shown, an electronic device 300 according to an embodiment of the present invention includes a processor 320, the processor 320 is coupled to a memory 310, and the memory 310 stores at least one computer program 330, and the at least one computer program 330 is loaded and executed by the processor 320, so that the electronic device 300 implements any of the above-mentioned landslide geological hazard assessment methods, specifically:

[0075] The electronic device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors 320 (Central Processing Units, CPU) and one or more memories 310, wherein at least one computer program 330 is stored in the one or more memories 310, and the at least one computer program 330 is loaded and executed by the one or more processors 320, so that the electronic device 300 implements any landslide geological hazard assessment method provided in the above embodiments. Of course, the electronic device 300 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output, and the electronic device 300 may also include other components for implementing device functions, which will not be described in detail here.

[0076] A computer-readable storage medium according to an embodiment of the present invention stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that a computer implements any of the above-mentioned landslide geological hazard assessment methods.

[0077] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0078] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device executes any of the above-mentioned landslide geological hazard assessment methods.

[0079] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects and represent the definition of a specific order or sequence. The order of use of similar objects can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0080] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0081] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0082] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A landslide geological disaster assessment method, characterized in that: include: Determine a plurality of target evaluation factors associated with landslide geological hazards in a target area, and construct and train a landslide geological hazard evaluation model for the target area based on all target evaluation factors; The current geological environment data of the target area is input into the trained landslide geological hazard assessment model to obtain the landslide geological hazard assessment result of the target area.

2. The landslide geological disaster assessment method according to claim 1, characterized in that: The steps of determining a plurality of target evaluation factors associated with landslide geological hazards in a target area include: A plurality of initial evaluation factors for evaluating landslide geological hazards in the target area are selected, and a plurality of target evaluation factors are screened from all the initial evaluation factors by using the principal component analysis method and combining the specific value of each initial evaluation factor.

3. The landslide geological disaster assessment method according to claim 1, characterized in that: The step of constructing a landslide geological hazard assessment model for the target area based on all target assessment factors includes: The landslide geological hazard evaluation model of the target area is constructed by using the entropy index method and the random forest algorithm and combining the specific value of each target evaluation factor.

4. The landslide geological disaster assessment method according to any one of claims 1 to 3, characterized in that: Also includes: Obtaining the ROC curve of the trained landslide geological hazard assessment model, and judging whether the area between the ROC curve and the horizontal axis of the trained landslide geological hazard assessment model exceeds a preset value, and obtaining a judgment result; When the judgment result is yes, it is determined that the accuracy of the trained landslide geological hazard assessment model meets the requirements, and the step of inputting the current environmental data of the target area into the trained landslide geological hazard assessment model to obtain the landslide geological hazard assessment result of the target area is executed.

5. The landslide geological disaster assessment method according to claim 4, characterized in that: The landslide geological hazard evaluation model is the RF-IOE model.

6. A landslide geological disaster assessment system, characterized in that: include: Processing module and evaluation module; The processing module is used to: determine multiple target evaluation factors associated with landslide geological hazards in the target area, and based on all the target evaluation factors, construct and train a landslide geological hazard evaluation model for the target area; The evaluation module is used to: input the current geological environment data of the target area into the trained landslide geological disaster evaluation model to obtain the landslide geological disaster evaluation result of the target area.

7. The landslide geological disaster assessment system according to claim 6, characterized in that: The processing module is specifically used for: A plurality of initial evaluation factors for evaluating landslide geological hazards in the target area are selected, and a plurality of target evaluation factors are screened from all the initial evaluation factors by using the principal component analysis method and combining the specific value of each initial evaluation factor.

8. The landslide geological disaster assessment system according to claim 6, characterized in that: The processing module is specifically used for: The landslide geological hazard evaluation model of the target area is constructed by using the entropy index method and the random forest algorithm and combining the specific value of each target evaluation factor.

9. An electronic device, characterized in that: The electronic device includes a processor, the processor is coupled to a memory, at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the electronic device implements the landslide geological hazard assessment method as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: At least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by the processor so that the computer-readable storage medium implements the landslide geological hazard assessment method as described in any one of claims 1 to 5.

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