Landslide risk evaluation method, device, equipment, medium and product

Abstract

The invention discloses a landslide risk evaluation method, device and equipment, a medium and a product, and relates to the field of landslide risk evaluation. The method comprises the following steps: determining feature data of each grid unit in a research area, wherein the feature data comprises geological data, geographic data and environmental data; multi-dimensional factors of landslide formation are considered, and an evaluation index system of four target factors in a landslide four-factor theory is established; the four target factors comprise dangerousness, exposure, vulnerability and disaster prevention and reduction capability; determining a risk quantized value of each target factor in each grid unit based on an evaluation index system of the four target factors; determining the weight of each target factor by using a chromatographic analysis method; and determining a landslide risk index of each grid unit in the research area by combining the risk quantized value and the weight of each target factor in each grid unit, and obtaining a landslide risk level of each grid unit in the research area by adopting a natural discontinuity method. The landslide risk can be comprehensively evaluated.

Description

Technical Field

[0001] The present application relates to the field of landslide risk assessment, and in particular to a landslide risk assessment method, device, equipment, medium and product. Background Art

[0002] Landslide risk assessment refers to the systematic prediction and evaluation of the possibility, impact, and post-disaster losses of landslides in a certain area or region through scientific analysis methods and tools. At present, the research on landslide risk assessment is in the development stage. Landslide research mainly involves susceptibility, hazard, vulnerability, and risk. The evaluation methods are mainly qualitative, quantitative, and a combination of qualitative and quantitative methods, including frequency ratio method, information volume method, logistic regression method, hierarchical analysis method, artificial neural network method, random forest method, information volume model method, etc. However, traditional methods fail to fully consider regional differences, environmental diversity, and the comprehensiveness of indicator factor selection, resulting in the limitation of the accuracy and applicability of risk assessment results, thus affecting the effectiveness of landslide early warning, risk management, and disaster reduction strategies. Summary of the invention

[0003] The purpose of this application is to provide a landslide risk assessment method, device, equipment, medium and product, which can comprehensively evaluate landslide risks.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a landslide risk assessment method, comprising: collecting characteristic data of a study area, and determining characteristic data of each grid unit in the study area; the characteristic data include geological data, geographical data and environmental data; considering the multi-dimensional factors of landslide formation, establishing an evaluation index system of four target factors in the four-factor theory of landslide; the four target factors include hazard, exposure, vulnerability and disaster prevention and mitigation capabilities; according to the characteristic data of each grid unit in the study area, obtaining the data of each indicator factor in the evaluation index system of the four target factors in each grid unit; according to the data of each indicator factor in the evaluation index system of each target factor, determining the risk quantification value of each target factor in each grid unit; determining the weight of each target factor by using the tomography analysis method; combining the risk quantification value of each target factor in each grid unit and the weight of each target factor, determining the landslide risk index of each grid unit in the study area; according to the landslide risk index of each grid unit in the study area, using the natural break method to obtain the landslide risk level of each grid unit in the study area.

[0006] In a second aspect, the present application provides a landslide risk assessment device, including: a collection module, an establishment module, a grid data determination module, a risk quantification module, a weight calculation module, a landslide risk index determination module and a grade classification module.

[0007] A collection module is used to collect characteristic data of the study area and determine the characteristic data of each grid unit in the study area; the characteristic data include geological data, geographical data and environmental data; an establishment module is used to consider the multi-dimensional factors of landslide formation and establish an evaluation index system of four target factors in the four-factor theory of landslide; the four target factors include hazard, exposure, vulnerability and disaster prevention and mitigation capabilities; a grid data determination module is used to obtain the data of each indicator factor in the evaluation index system of the four target factors in each grid unit according to the characteristic data of each grid unit in the study area; a risk quantification module is used to determine the risk quantification value of each target factor in each grid unit according to the data of each indicator factor in the evaluation index system of each target factor; a weight calculation module is used to determine the weight of each target factor by using the tomography analysis method; a landslide risk index determination module is used to determine the landslide risk index of each grid unit in the study area by combining the risk quantification value of each target factor in each grid unit and the weight of each target factor; a grade classification module is used to obtain the landslide risk grade of each grid unit in the study area by using the natural break method according to the landslide risk index of each grid unit in the study area.

[0008] In a third aspect, the present application provides 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 above-mentioned landslide risk assessment method.

[0009] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned landslide risk assessment method when executed by a processor.

[0010] In a fifth aspect, the present application provides a computer program product, including a computer program, which implements the above-mentioned landslide risk assessment method when executed by a processor.

[0011] According to the specific embodiments provided in this application, this application has the following technical effects:

[0012] The present application provides a landslide risk assessment method, device, equipment, medium and product, which considers the comprehensiveness of the selection of indicator factors, and focuses on the four key indicators of landslide hazard, exposure, vulnerability and disaster prevention and mitigation capabilities, so as to more comprehensively analyze the landslide risk; and establishes an evaluation indicator system of four target factors from multiple dimensions, and the selected indicator factors reflect the complexity of landslide formation; at the same time, the characteristic data of the research area collected include geological data, geographical data and environmental data, which fully considers the regional differences, environmental diversity, etc., and can realize a comprehensive and integrated evaluation of landslide risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments will be briefly introduced below. 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 paying creative work.

[0014] Figure 1 This is an application environment diagram of a landslide risk assessment method in one embodiment of the present application;

[0015] Figure 2 A flow chart of a landslide risk assessment method provided in one embodiment of the present application;

[0016] Figure 3 This is a schematic diagram of the landslide risk level distribution in a certain study area in 2022;

[0017] Figure 4 A more detailed flowchart of a landslide risk assessment method provided in another embodiment of the present application;

[0018] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0020] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0021] The landslide risk assessment method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown, the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the characteristic data of the study area to the server 104. After the server 104 receives the characteristic data of the study area, the server 104 determines the characteristic data of each grid unit in the study area for the characteristic data of the study area; the characteristic data includes geological data, geographical data and environmental data; considering the multi-dimensional factors of landslide formation, an evaluation index system of four target factors in the four-factor theory of landslide is established; the four target factors include hazard, exposure, vulnerability and disaster prevention and mitigation capabilities; according to the characteristic data of each grid unit in the study area, the data of each indicator factor in the evaluation index system of the four target factors in each grid unit are obtained; according to the data of each indicator factor in the evaluation index system of each target factor, the risk quantification value of each target factor in each grid unit is determined; the weight of each target factor is determined by using the tomography analysis method; the landslide risk index of each grid unit in the study area is determined by combining the risk quantification value of each target factor in each grid unit and the weight of each target factor; according to the landslide risk index of each grid unit in the study area, the landslide risk level of each grid unit in the study area is obtained by using the natural break method. The server 104 may feed back the obtained landslide risk index and landslide risk level to the terminal 102. In addition, in some embodiments, the landslide risk assessment method may also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 may directly perform landslide risk assessment on the characteristic data of the study area, or the server 104 may obtain the characteristic data of the study area from the data storage system and perform landslide risk assessment on the characteristic data of the study area.

[0022] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0023] In an exemplary embodiment, Figure 2 As shown, a landslide risk assessment method is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1The server 104 in the example is used as an example to illustrate, including the following steps 201 to 207. Among them:

[0024] Step 201: Collect characteristic data of the study area and determine characteristic data of each grid cell in the study area; the characteristic data includes geological data, geographical data and environmental data.

[0025] Step 202: Considering the multi-dimensional factors of landslide formation, an evaluation index system of four target factors in the four-factor theory of landslide is established; the four target factors include hazard, exposure, vulnerability and disaster prevention and mitigation capabilities.

[0026] Step 203: According to the characteristic data of each grid cell in the study area, the data of each index factor in each grid cell in the evaluation index system of the four target factors are obtained.

[0027] Step 204: Determine the risk quantification value of each target factor in each grid unit according to the data of each indicator factor in the evaluation indicator system of each target factor.

[0028] Step 205: Determine the weight of each target factor using the tomographic analysis method.

[0029] Step 206: Determine the landslide risk index of each grid cell in the study area by combining the risk quantification value of each target factor in each grid cell and the weight of each target factor.

[0030] Step 207: According to the landslide risk index of each grid unit in the study area, the landslide risk level of each grid unit in the study area is obtained by using the natural break method.

[0031] By implementing the above steps 201 to 207, the risk assessment process is quantitatively analyzed to more accurately reflect the complexity and diversity of landslide hazards, which is more conducive to landslide risk assessment in areas with complex geological environments and developed landslides. It can more comprehensively understand the occurrence mechanism and spatiotemporal distribution of landslides, propose effective early warnings and formulate feasible response strategies, protect the ecological environment, and maintain the sustainable development of local society.

[0032] In another exemplary embodiment of the present application, the feature data of step 201 above includes geological data, geographic data, environmental data, and socio-economic data. The geographic data may include basic geographic data, road and water distribution data. The environmental data includes climate, land, and vegetation data.

[0033] Data source:

[0034] (1) Basic geographic data: Download the full coverage “ASTERGDEM” digital elevation product from the geospatial data cloud platform, with a data spatial resolution of 30m.

[0035] (2) Road and water system distribution data are downloaded from the OSM platform, and the collected historical road network and water system data are stored in a database by year.

[0036] (3) Geological data: Engineering rock formations, faults, and earthquake peak acceleration data were downloaded from the Resource and Environmental Data Cloud Platform of the Chinese Academy of Sciences.

[0037] (4) Climate, land, and vegetation data: Precipitation and land use data were downloaded from the Qinghai-Tibet Plateau Science Data Center, and vegetation coverage data were downloaded from NASA.

[0038] In another exemplary embodiment of the present application, regular square grid cells are used as evaluation units for landslide hazard assessment, and specific attribute values ​​are assigned to each grid cell, which is conducive to effective calculation and analysis of data.

[0039] In another exemplary embodiment of the present application, the present application selects 12 landslide hazard index factors to evaluate landslide hazard, and these index factors comprehensively reflect the characteristics of the natural environment and the impact of human activities. The index factors in the evaluation index system of hazard include: elevation, slope aspect, slope, undulation, vegetation coverage, average annual rainfall, lithology type, distance from fault, road density, water system density, earthquake peak acceleration and land use status.

[0040] Exposure refers to the degree to which elements such as population, property, infrastructure and natural environment in a specific area are exposed to the threat of landslides. It reflects the scope and scale of damage that these elements may suffer and is an important part of landslide risk assessment. This application divides the exposure index into economic exposure (gross regional product), social exposure (population density, population size), and environmental exposure (farmland area, building land area, forest area, grassland area). That is, this application selects 7 indicator factors to evaluate landslide exposure. The indicator factors in the exposure evaluation index system include: gross regional product, population density, population size, farmland area, building land area, forest area and grassland area.

[0041] Vulnerability aims to further explore the response and recovery capabilities of identified exposure factors when disasters occur. This application takes into account multiple factors such as the natural environment, socio-economic conditions, and population composition, and divides the vulnerability indicators into economic vulnerability (proportion of primary industry, grain output, total output value of agriculture, forestry, animal husbandry, and fishery), social vulnerability (proportion of agricultural population, number of industrial enterprises above designated size), and environmental vulnerability (proportion of cultivated land area). That is, this application selects 6 indicator factors to evaluate landslide vulnerability. The indicator factors in the vulnerability evaluation index system include: proportion of primary industry, grain output, total output value of agriculture, forestry, animal husbandry, and fishery, proportion of agricultural population, number of industrial enterprises above designated size, and proportion of cultivated land area.

[0042] Disaster prevention and mitigation capability refers to the comprehensive capability of a region or organization to prevent, respond, mitigate and recover from natural disasters (such as geological disasters, floods, typhoons, etc.). It includes the ability to take effective measures before, during and after a disaster to minimize casualties, property losses and social impacts. This application is based on three criteria: government disaster prevention investment, medical and health level, and residents' emergency response. It selects 9 indicator factors, including total local fiscal expenditure, number of geological disaster risk points, number of health institutions, number of beds per thousand people, number of health technicians per thousand people, number of doctors per thousand people, number of nurses per thousand people, per capita disposable income of rural residents, and per capita disposable income of urban residents, to analyze landslide disaster prevention and mitigation capability.

[0043] In another exemplary embodiment of the present application, the above step 204 determines the risk quantification value of the hazard in each grid cell according to the data of each indicator factor in the hazard evaluation index system in each grid cell, which can be replaced by the following steps 301 to 306:

[0044] Step 301: Divide the indicators in the risk evaluation index system into positive indicators, negative indicators and appropriate indicators.

[0045] For the 12 risk index factors selected in this application, the positive factors that cause landslides are elevation, slope, undulation, earthquake peak acceleration, and annual average rainfall; the negative factors are distance to roads, distance to water systems, and distance to faults; the appropriate factors are slope direction and vegetation coverage. Among them, lithology and land use are assigned using the fuzzy assignment method.

[0046] Step 302: Based on the data of the positive index in each grid cell, the data of the negative index in each grid cell and the data of the appropriate index in each grid cell in the risk evaluation index system, the index data are normalized using the range method to obtain the normalized data of each index factor in each grid cell in the risk evaluation index system.

[0047] Range normalization method: This application uses the range method to normalize the index data, converting the absolute value of the evaluation index into a relative value to eliminate the difference in measurement units. Landslide hazard evaluation indicators are usually divided into positive indicators, negative indicators and appropriateness indicators. Assume that the data matrix is ​​X, where X ij represents the value of the i-th sample on the j-th feature. The normalization formula of the positive indicator is as follows (where is the normalized value):

[0048]

[0049] The normalization formula for negative indicators is as follows (where is the normalized value):

[0050]

[0051] The normalization formula for the appropriateness index is as follows (where is the normalized value, represents the optimal value of the indicator):

[0052]

[0053] Step 303: Based on the normalized data of each indicator factor in each grid unit in the risk evaluation indicator system, the frequency ratio method is used to determine the frequency ratio of each index factor in each level in each grid unit.

[0054] This application uses the frequency ratio method to carry out landslide hazard assessment research. The frequency ratio method divides a certain hazard index factor X into i types or i levels according to a reasonable spatial analysis of the relationship between the occurrence of landslides. The formula is:

[0055]

[0056] Where: FR i is the frequency ratio of the i-th type or i-th level of the indicator factor, P(H i X i ) indicates H i (Disaster) of a certain level X i The frequency of occurrence, X i represents the risk factor of the i-th type or i-th level, P(X i ) indicates that X i The frequency, Represents factor X i Medium H i The number or area of Indicates H i The total number or total area of N iThe total area, represents the total area of ​​the study area. i >1, indicating that P(H i |X i )>P(H i ), that is, X i Good for disasters i The occurrence of disasters is not conducive to i occurs.

[0057] Step 304: Use the principal component analysis method to determine the weight of each indicator factor in the risk evaluation indicator system.

[0058] Step 305: multiply the frequency ratio of each index factor at each level in each grid unit by the weight of each index factor to obtain the quantitative value of each index factor at each level in each grid unit.

[0059] Step 306: Add the quantitative values ​​of each level of all indicator factors in each grid unit to obtain the risk quantitative value of the dangerousness in each grid unit.

[0060] According to the weights of various index factors of landslide hazard and the frequency ratios of various grades of index factors, the specific quantitative values ​​of each grade within each index factor can be obtained by multiplying them. The quantitative values ​​are assigned to the grids of the corresponding index factors, and then the raster data of each index factor are added using ArcGIS to obtain the quantitative risk value of the hazard in each grid unit.

[0061] In another exemplary embodiment of the present application, the present application uses the range method to normalize each factor in the evaluation of landslide exposure, vulnerability and disaster prevention and mitigation capabilities, and uses the entropy method model for evaluation. Then, in the above step 204, the risk quantification value of each target factor other than dangerousness in each grid unit is determined according to the data of each indicator factor in the evaluation index system of each target factor other than dangerousness in each grid unit, which can be replaced by the following steps 401 to 404:

[0062] Step 401: Divide the indicators in the evaluation index system of each target factor other than risk into positive indicators and negative indicators.

[0063] Among the selected exposure index factors, for population, population density, farmland area and building land area, the larger the value, the greater the positive effect on exposure, so these indicators are positive indicators; while regional GDP, forest area and grassland area are not conducive to the occurrence of landslides, so these values ​​are negative indicators. After normalizing the above index factors using the range method, the entropy method is used to calculate the information entropy value, information utility value and weight of each index factor in each year.

[0064] After normalizing the positive indicators (the proportion of agricultural population) and negative indicators (the proportion of primary industry, the proportion of cultivated land area, grain output, the total output value of agriculture, forestry, animal husbandry and fishery, and the number of industrial enterprises above designated size) using the range method, the entropy method was used to calculate the information entropy value, information utility value and weight of each indicator factor.

[0065] Step 402: Based on the data of positive indicators in each grid cell and the data of negative indicators in each grid cell in the evaluation index system of each target factor other than dangerousness, the index data are normalized using the range method to obtain the normalized data of each indicator factor in each grid cell in the evaluation index system of each target factor other than dangerousness.

[0066] Step 403: According to the normalized data of each indicator factor in each grid unit in the evaluation indicator system of each target factor other than dangerousness, the weight of each indicator factor in the evaluation indicator system of each target factor other than dangerousness is determined by using the entropy method.

[0067] This application uses the size of the entropy value to evaluate the discrete degree of various indicators that affect the landslide exposure capacity, and reflects the comprehensive exposure through a comprehensive index.

[0068] For the data matrix standardized by the range method, the information entropy of each indicator is calculated. The formula of the entropy method is as follows:

[0069]

[0070] Among them, E j is the information entropy of the jth indicator, n is the number of evaluation objects, p ij is the standardized value of the i-th evaluation object on the j-th indicator. To avoid the appearance of 0 in the logarithm, p is calculated in the actual calculation. ij Make adjustments, usually p ij +∈, where ∈ is a small positive number. The evaluation object refers to the evaluation indicators of landslide exposure, vulnerability and disaster prevention and mitigation capabilities.

[0071] The information utility value formula of each indicator is:

[0072] D j =1-E j .

[0073] Among them, D j is the information utility value of the j-th indicator.

[0074] Finally, the weight of each indicator is calculated according to its information utility value:

[0075]

[0076] Among them, W jis the weight of the jth indicator, and m is the total number of indicators.

[0077] Step 404: Based on the normalized data of each indicator factor in each grid cell and the weight of each indicator factor in the evaluation indicator system of each target factor other than risk, according to the formula Calculate the risk quantification value of each target factor other than the hazard in each grid cell; where E is the risk quantification value of the target factor other than the hazard in each grid cell, T i is the normalized data of the i-th indicator factor in each grid cell in the evaluation index system of target factors other than risk, W i is the weight of the ith indicator factor in the evaluation index system of target factors other than risk, and n is the number of indicator factors in the evaluation index system of target factors other than risk.

[0078] In another exemplary embodiment of the present application, the above step 205 uses the analytic hierarchy process to assess the landslide risk, and the steps are as follows:

[0079] 1. Establish a hierarchical model

[0080] Landslide risk is the product of the combined effects of four factors: hazard, exposure, vulnerability and disaster prevention and mitigation. According to the relationship between them, it is divided into the highest level (target level), the middle level (indicators closely related to the target - criterion level) and the lowest level (factors affecting indicators - indicator level). The analytic hierarchy process is used to construct the risk assessment model.

[0081] This application takes landslide risk as the target layer, landslide hazard, landslide exposure, landslide vulnerability and landslide disaster prevention and mitigation capabilities as the criterion layer, and takes 12 factors affecting landslide hazard, 7 factors affecting landslide exposure, 6 factors affecting landslide vulnerability, and 9 factors affecting landslide disaster prevention and mitigation capabilities, a total of 34 indicator factors as the indicator layer.

[0082] 2. Construct judgment matrix

[0083] The judgment matrix has the following properties (a ij is the comparison result of the importance of factor i and factor j):

[0084]

[0085] This application assigns values ​​and scores to hazard, exposure, vulnerability and disaster prevention and mitigation capabilities based on a judgment matrix ratio scale.

[0086] 3. Hierarchical single sorting and consistency test

[0087] Corresponding to the maximum characteristic root λ of the judgment matrix maxThe eigenvector of is normalized (the sum of the elements in the vector is equal to 1) and recorded as W. The elements of W are the ranking weights of the relative importance of the factors at the same level to a factor at the previous level. This process is called hierarchical single sorting. Whether the hierarchical single sorting can be confirmed requires a consistency test. The so-called consistency test refers to the allowable range of inconsistency determined for A. Among them, the only non-zero eigenvalue of the n-order consistent matrix is ​​n; the maximum eigenvalue λ≥n of the n-order positive reciprocal matrix A, A is a consistent matrix if and only if λ=n.

[0088] Since λ continuously depends on a ij , the more λ is larger than n, the more serious the inconsistency of A. The consistency index is calculated by CI. The smaller the CI, the greater the consistency. The eigenvector corresponding to the maximum eigenvalue is used as the weight vector of the influence of the compared factor on a certain factor in the upper layer. The greater the inconsistency, the greater the judgment error caused. Therefore, the value of λ-n can be used to measure the inconsistency of A. The consistency index is defined as:

[0089]

[0090] When CI = 0, there is complete consistency; when CI is close to 0, there is satisfactory consistency; the larger the CI, the more serious the inconsistency.

[0091] In order to measure the size of CI, the random consistency index RI is introduced:

[0092]

[0093] Among them, the random consistency index RI is related to the order of the judgment matrix. Generally speaking, the larger the matrix order, the greater the possibility of random deviation from consistency.

[0094] Considering that the deviation from consistency may be caused by random reasons, when checking whether the judgment matrix has satisfactory consistency, it is also necessary to compare CI and random consistency index RI to obtain the test coefficient CR, the formula is as follows:

[0095]

[0096] Generally, if CR < 0.1, the judgment matrix is ​​considered to pass the consistency test, otherwise it does not have satisfactory consistency.

[0097] In another exemplary embodiment of the present application, landslide risk is the product of the combined effects of landslide hazard, landslide exposure, landslide vulnerability and landslide disaster prevention and mitigation capabilities. Based on this, the present application starts from the four aspects of landslide hazard, exposure, vulnerability and disaster prevention and mitigation capabilities to construct a reasonable risk assessment model. The calculation formula of the risk assessment model is:

[0098]

[0099] Where Z is the landslide risk index of each grid unit in the study area, E1 is the risk quantification value of hazard in each grid unit, W1 is the weight of hazard, E2 is the risk quantification value of exposure in each grid unit, W2 is the weight of exposure, E3 is the risk quantification value of vulnerability in each grid unit, W3 is the weight of vulnerability, E4 is the risk quantification value of disaster prevention and mitigation capability in each grid unit, W4 is the weight of disaster prevention and mitigation capability.

[0100] In another exemplary embodiment of the present application, classification is performed based on the natural discontinuity method, and finally the risk distribution of five levels, namely, extremely high, high, medium, low, and extremely low, is obtained. The spatial distribution of each indicator of disaster risk is visualized through the ArcGIS software platform, and then the appropriate grid scale is selected to construct layers of different indicators, and these layers are calculated and superimposed by a specific calculation method. Finally, the risk index of each grid is calculated, and the disaster risk is visualized.

[0101] Figure 3 This is a schematic diagram of the landslide risk level distribution in a certain study area in 2022.

[0102] In another exemplary embodiment of the present application, after the above step 204, the method may further include: obtaining the risk level of each target factor in each grid unit by using a natural discontinuity method according to the risk quantification value of each target factor in each grid unit.

[0103] The natural breakpoint method is used to divide the danger into five categories: extremely high danger, high danger, medium danger, low danger, and extremely low danger; the exposure is classified into five categories: extremely high exposure, high exposure, medium exposure, low exposure, and extremely low exposure; the vulnerability is classified into five categories: extremely high vulnerability, high vulnerability, medium vulnerability, low vulnerability, and extremely low vulnerability; the disaster prevention and mitigation capabilities are reclassified into five categories: extremely high disaster prevention and mitigation capabilities, high disaster prevention and mitigation capabilities, medium disaster prevention and mitigation capabilities, low disaster prevention and mitigation capabilities, and extremely low disaster prevention and mitigation capabilities.

[0104] Figure 3 A more detailed process of a landslide risk assessment method of the present application is shown. It aims to comprehensively evaluate landslide risks and provide a more scientific risk assessment framework. The model establishes a landslide risk assessment system from multiple dimensions. The selected indicator factors reflect the complexity of landslide formation. The frequency ratio-principal component analysis method and entropy weight method are used to quantify the landslide hazard, exposure, vulnerability and disaster prevention and mitigation capabilities, and the landslide risk results are coupled with the hierarchical analysis method. Finally, the risk results are visualized. Through scientific and systematic risk identification and management, it helps all sectors of society to better prevent and respond to landslides, effectively reduce the losses caused by disasters, and promote social sustainable development and ecological environmental protection.

[0105] Focusing on the four key indicators of landslide hazard, exposure, vulnerability and disaster prevention and mitigation capacity, the landslide risk can be analyzed more comprehensively. The analytic hierarchy process can decompose complex problems into a hierarchical structure, which is easy to analyze and understand. At the same time, it combines qualitative and quantitative methods to transform subjective judgments into quantitative results, improves the scientificity and reliability of decision-making, and is suitable for multi-criteria decision-making problems that are difficult to quantify. The advantage of the entropy weight method is that it can objectively extract weights from the data and avoid the influence of subjective factors on weight distribution. By calculating the entropy value of each indicator, the information contribution of each indicator is reflected, and the weight distribution is more scientific and reasonable. The advantage of the frequency ratio is that it is simple and intuitive. By calculating the ratio of the frequency of disaster occurrence to the frequency of related factors, it can effectively reveal the correlation between specific factors and disaster occurrence. This method not only avoids complex model assumptions, but also quantifies risks in a data-driven way, helps identify high-risk areas, and optimizes resource allocation. The advantage of the principal component analysis method is that it can reduce the dimensionality of high-dimensional data, simplify the data structure, and retain as much original information as possible. By extracting the most representative principal components from the data, PCA (Principal Component Analysis) reduces redundant information, reduces the complexity of the data, and facilitates subsequent analysis and visualization. It helps to reveal the intrinsic relationship between variables, reduce noise and correlation, and improve data processing efficiency. This application uses these methods in combination to improve the scientificity, accuracy, and comprehensiveness of landslide risk assessment.

[0106] Based on the same inventive concept, the embodiment of the present application also provides a landslide risk assessment device for implementing the landslide risk assessment method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more landslide risk assessment device embodiments provided below can refer to the limitations of the landslide risk assessment method above, and will not be repeated here.

[0107] In an exemplary embodiment, a landslide risk assessment device is provided, which includes: a collection module, a creation module, a grid data determination module, a risk quantification module, a weight calculation module, a landslide risk index determination module and a grade classification module.

[0108] A collection module is used to collect characteristic data of the study area and determine the characteristic data of each grid unit in the study area; the characteristic data include geological data, geographical data and environmental data; an establishment module is used to consider the multi-dimensional factors of landslide formation and establish an evaluation index system of four target factors in the four-factor theory of landslide; the four target factors include hazard, exposure, vulnerability and disaster prevention and mitigation capabilities; a grid data determination module is used to obtain the data of each indicator factor in the evaluation index system of the four target factors in each grid unit according to the characteristic data of each grid unit in the study area; a risk quantification module is used to determine the risk quantification value of each target factor in each grid unit according to the data of each indicator factor in the evaluation index system of each target factor; a weight calculation module is used to determine the weight of each target factor by using the tomography analysis method; a landslide risk index determination module is used to determine the landslide risk index of each grid unit in the study area by combining the risk quantification value of each target factor in each grid unit and the weight of each target factor; a grade classification module is used to obtain the landslide risk grade of each grid unit in the study area by using the natural break method according to the landslide risk index of each grid unit in the study area.

[0109] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. 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. 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 database of the computer device is used to store landslide risk indexes and landslide risk levels. The input / output interface of the computer device is used to exchange information between the processor and an external device. 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, a landslide risk assessment method is implemented.

[0110] Those skilled in the art will understand that Figure 5The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0111] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0112] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0113] 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 used 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.

[0114] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0115] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0116] The technical features of the above embodiments may 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.

[0117] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A landslide risk assessment method, characterized in that: include: Collect characteristic data of the study area and determine characteristic data of each grid unit in the study area; the characteristic data includes geological data, geographical data and environmental data; Considering the multi-dimensional factors of landslide formation, an evaluation index system of four target factors in the four-factor theory of landslide is established; the four target factors include hazard, exposure, vulnerability and disaster prevention and mitigation capabilities; According to the characteristic data of each grid unit in the study area, the data of each index factor in each grid unit of the evaluation index system of the four target factors are obtained; According to the data of each indicator factor in the evaluation index system of each target factor, determine the risk quantification value of each target factor in each grid unit; The weight of each target factor was determined by using the tomographic analysis method; Combining the risk quantification value of each target factor in each grid unit and the weight of each target factor, the landslide risk index of each grid unit in the study area is determined; According to the landslide risk index of each grid unit in the study area, the landslide risk level of each grid unit in the study area was obtained by using the natural break method.

2. The landslide risk assessment method according to claim 1, characterized in that: The index factors in the risk assessment index system include: elevation, slope aspect, slope gradient, undulation, vegetation coverage, average annual rainfall, lithology type, distance from faults, road density, water system density, earthquake peak acceleration and land use status; The index factors in the exposure evaluation index system include: regional GDP, population density, population size, farmland area, building land area, forest area and grassland area; The indicator factors in the vulnerability evaluation index system include: the proportion of primary industry, grain output, total output value of agriculture, forestry, animal husbandry and fishery, the proportion of agricultural population, the number of industrial enterprises above designated size and the proportion of cultivated land area; The indicator factors in the evaluation index system of disaster prevention and mitigation capabilities include: total local fiscal expenditure, the number of geological disaster risk points, the number of health institutions, the number of hospital beds per thousand people, the number of health technicians per thousand people, the number of doctors per thousand people, the number of nurses per thousand people, the per capita disposable income of rural residents and the per capita disposable income of urban residents.

3. The landslide risk assessment method according to claim 1, characterized in that: According to the data of each index factor in each grid unit in the risk evaluation index system, the risk quantitative value of the risk in each grid unit is determined, including: The indicators in the risk evaluation index system are divided into positive indicators, negative indicators and moderate indicators; According to the data of positive indicators in each grid cell, negative indicators in each grid cell and appropriate indicators in each grid cell in the risk evaluation index system, the index data are normalized by using the range method to obtain the normalized data of each index factor in each grid cell in the risk evaluation index system; According to the normalized data of each indicator factor in each grid unit in the risk assessment index system, the frequency ratio method is used to determine the frequency ratio of each indicator factor in each grade in each grid unit; The principal component analysis method is used to determine the weight of each indicator factor in the risk evaluation index system; Multiply the frequency ratio of each index factor at each level in each grid unit by the weight of each index factor to obtain the quantitative value of each index factor at each level in each grid unit; The quantitative values ​​of each grade of all indicator factors in each grid unit are added together to obtain the quantitative risk value of the hazard in each grid unit.

4. The landslide risk assessment method according to claim 1, characterized in that: According to the data of each indicator factor in each grid unit in the evaluation index system of each target factor other than the hazard, the risk quantification value of each target factor other than the hazard in each grid unit is determined, including: Divide the indicators in the evaluation index system of each target factor other than risk into positive indicators and negative indicators; According to the data of positive indicators in each grid cell and the data of negative indicators in each grid cell in the evaluation index system of each target factor other than dangerousness, the index data are normalized by using the range method to obtain the normalized data of each index factor in each grid cell in the evaluation index system of each target factor other than dangerousness; According to the normalized data of each indicator factor in each grid unit in the evaluation indicator system of each target factor other than danger, the weight of each indicator factor in the evaluation indicator system of each target factor other than danger is determined by using the entropy method; According to the normalized data of each indicator factor in each grid cell and the weight of each indicator factor in the evaluation index system of each target factor other than danger, according to the formula Calculate the risk quantification value of each target factor other than the hazard in each grid cell; where E is the risk quantification value of the target factor other than the hazard in each grid cell, T i is the normalized data of the i-th index factor in each grid cell in the evaluation index system of target factors other than risk, W i is the weight of the ith indicator factor in the evaluation index system of target factors other than risk, and n is the number of indicator factors in the evaluation index system of target factors other than risk.

5. The landslide risk assessment method according to claim 1, characterized in that: The calculation formula for the landslide risk index of each grid cell in the study area is: Where Z is the landslide risk index of each grid unit in the study area, E1 is the risk quantification value of hazard in each grid unit, W1 is the weight of hazard, E2 is the risk quantification value of exposure in each grid unit, W2 is the weight of exposure, E3 is the risk quantification value of vulnerability in each grid unit, W3 is the weight of vulnerability, E4 is the risk quantification value of disaster prevention and mitigation capability in each grid unit, W4 is the weight of disaster prevention and mitigation capability.

6. The landslide risk assessment method according to claim 1, characterized in that: According to the data of each indicator factor in the evaluation index system of each target factor, the risk quantification value of each target factor in each grid unit is determined, and then it also includes: According to the risk quantification value of each target factor in each grid unit, the natural discontinuity method is used to obtain the risk level of each target factor in each grid unit.

7. A landslide risk assessment device, characterized in that: The landslide risk assessment device comprises: A collection module is used to collect characteristic data of the study area and determine the characteristic data of each grid unit in the study area; the characteristic data includes geological data, geographical data and environmental data; Establish a module to consider the multi-dimensional factors of landslide formation and establish an evaluation index system for the four target factors in the four-factor theory of landslide; the four target factors include hazard, exposure, vulnerability and disaster prevention and mitigation capabilities; A grid data determination module is used to obtain the data of each index factor in each grid unit in the evaluation index system of four target factors according to the characteristic data of each grid unit in the study area; The risk quantification module is used to determine the risk quantification value of each target factor in each grid unit according to the data of each indicator factor in the evaluation indicator system of each target factor; A weight calculation module is used to determine the weight of each target factor using tomographic analysis; The landslide risk index determination module is used to determine the landslide risk index of each grid cell in the study area by combining the risk quantification value of each target factor in each grid cell and the weight of each target factor; The grade classification module is used to obtain the landslide risk level of each grid unit in the study area by using the natural break method according to the landslide risk index of each grid unit in the study 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 landslide risk 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 landslide risk assessment method 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 landslide risk assessment method described in any one of claims 1 to 6 is implemented.

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