Regional landslide disaster meteorological risk early warning method, storage medium and computer equipment

Abstract

The invention discloses a regional landslide disaster meteorological risk early warning method, a storage medium and computer equipment, and relates to the technical field of landslide disaster risk assessment, and the regional landslide disaster meteorological risk early warning method mainly comprises the steps: carrying out the evaluation of a slope unit through an information amount model, and obtaining a susceptibility grade, a rainfall threshold model is constructed according to hourly rainfall data inducing the rainfall type landslide disaster, and then the danger early warning level of each slope unit is obtained; and according to related economic and population data, constructing an economic loss disaster-bearing body grade and a population casualty disaster-bearing body grade of each slope unit, and generating a risk early-warning grade of each slope unit in combination with the risk early-warning grade. By implementing the regional landslide disaster meteorological risk early warning method, the storage medium and the computer equipment provided by the invention, the accuracy of landslide disaster early warning can be improved.

Description

Technical Field

[0001] This invention relates to the field of landslide disaster risk assessment technology, and more specifically, to a method, storage medium, and computer equipment for early warning of meteorological risks of regional landslide disasters. Background Technology

[0002] Research on early warning of rainfall-induced landslide disasters has made significant progress. Existing research on meteorological early warning of landslide disasters can be divided into two categories: one is to use statistical analysis methods to analyze the relationship between historical landslide events and rainfall, and construct rainfall threshold models to realize meteorological early warning of landslide disasters; the other is to use numerical simulation methods based on physical processes to realize early warning of landslide disasters by simulating processes such as rainfall infiltration and soil saturation.

[0003] Although current research on meteorological early warning for landslide disasters is relatively comprehensive, it still has significant limitations. First, existing methods focus on analyzing the quantitative relationship between landslides and rainfall, using various statistical methods to construct threshold models or numerical simulations based on physical processes to assess landslide risk, while neglecting the impact of the affected organism on landslide risk. Second, the research results only reflect the probability of disaster occurrence and cannot assess the social losses caused by the disaster.

[0004] Therefore, developing a landslide disaster risk early warning method that combines landslide susceptibility, rainfall threshold, and disaster-bearing body level to optimize the hazard warning results is of urgent and important practical significance for improving the accuracy of meteorological early warning of landslide disasters and reducing loss of life and property. Summary of the Invention

[0005] The purpose of this invention is to provide a method, storage medium, and computer equipment for early warning of meteorological risks of regional landslide disasters, which can improve the accuracy of landslide disaster early warning.

[0006] This invention provides a method for early warning of meteorological risks of regional landslide disasters, comprising the following steps: S1: Obtaining slope units based on geological environment data and historical landslide disaster data, and evaluating the slope units using an information volume model to obtain a susceptibility level; S2: Constructing a rainfall threshold model based on hourly rainfall data of induced rainfall-type landslide disasters, the rainfall threshold model including a threshold curve and a critical rainfall threshold; S3: Obtaining a hazard warning level for each slope unit based on the susceptibility level and the rainfall threshold model; S4: Constructing an economic loss-bearing body evaluation index and an economic loss-bearing body level for each slope unit based on relevant economic data; S5: Constructing a population casualty-bearing body index and a population casualty-bearing body level for each slope unit based on relevant population data; S6: Generating a risk warning level for each slope unit based on the economic loss-bearing body level, the population casualty-bearing body level, and the hazard warning level.

[0007] Further, step S1 specifically includes: S11: acquiring geological environment data and historical landslide disaster data, wherein the geological environment data includes high-precision DEM data, soil and rock types, road distribution, river distribution, normalized vegetation index, etc.; S12: dividing slope units based on the high-precision DEM data using township-level administrative boundaries; S13: preprocessing the geological environment data and historical landslide disaster data to obtain preprocessed geological environment data and historical landslide disaster data; S14: using the preprocessed geological environment data and historical landslide disaster data... Pearson correlation coefficient was used to analyze the correlation between the indicators. Indicators with large absolute values ​​of correlation coefficients were removed, and the remaining indicators were used as landslide susceptibility evaluation factors. S15: The information content of the landslide susceptibility evaluation factors was calculated using the information content model. S16: The total information content value was obtained based on the information content of the landslide susceptibility evaluation factors. S17: Based on the total information content value and the spatial distribution of historical landslide disaster points, the region was divided into four levels: non-susceptible, low-susceptible, medium-susceptible, and high-susceptible. The mode of the susceptibility level within each slope unit was counted and used as the susceptibility level of the corresponding slope unit.

[0008] Further, step S2 specifically includes: S21: Screening landslide disaster points induced by rainfall and statistically analyzing the historical rainfall information of each landslide disaster point; S22: Based on the historical rainfall information of each landslide disaster point, determining the number of effective rainfall days in the early stage by statistically analyzing the correlation between the number of landslide events and the cumulative rainfall on different days after the landslide; S23: Based on the number of effective rainfall days in the early stage, determining the rainfall attenuation coefficient based on the Pearson correlation coefficient, and calculating the effective rainfall in the early stage; S24: Fitting the relationship curve between the effective rainfall in the early stage and the rainfall on the current day, classifying the warning level according to the probability of landslide disaster occurrence, determining the threshold curve and critical rainfall threshold under different warning levels, and obtaining the rainfall threshold model.

[0009] Further, step S3 specifically includes: S31: determining the rainfall level based on the threshold curve and previous rainfall and forecast rainfall, and constructing a landslide disaster meteorological hazard early warning matrix based on the susceptibility level and rainfall level; S32: obtaining the hazard early warning level of each slope unit based on the landslide disaster meteorological hazard early warning matrix.

[0010] Further, step S4 specifically includes: S41: Constructing an economic loss disaster-bearing body evaluation index based on relevant economic data; S42: Standardizing the economic loss disaster-bearing body evaluation index using the range method to obtain standardized economic loss disaster-bearing body evaluation index; S43: Performing KMO test and Bartlett's test of sphericity on the standardized economic loss disaster-bearing body evaluation index to obtain the tested economic loss disaster-bearing body evaluation index; S44: Based on the tested economic loss disaster-bearing body evaluation index, using factor analysis, extracting factors with a cumulative variance contribution rate of more than a preset percentage, and calculating the corresponding factor score coefficient matrix, variance contribution rate, and factor score; S45: Using the variance contribution rate of each factor as a weight, weighting and summing the scores of each factor to obtain the economic disaster-bearing body score of each slope unit; S46: Based on the economic disaster-bearing body score, using the quantile method to classify the economic loss disaster-bearing body of each slope unit into levels to obtain the economic loss disaster-bearing body level of each slope unit.

[0011] Further, step S5 specifically includes: S51: Constructing a population casualty disaster-bearing body index based on relevant population data; S52: Standardizing the population casualty disaster-bearing body index using the range method to obtain a standardized population casualty disaster-bearing body index; S53: Constructing a judgment matrix using the analytic hierarchy process (AHP) and calculating the weights of each index; S54: Calculating a comprehensive population casualty disaster-bearing body index using the weighted summation method based on the weights of each index and the standardized population casualty disaster-bearing body index; S55: Classifying the population casualty disaster-bearing body of each slope unit according to the comprehensive population casualty disaster-bearing body index using the quantile method to obtain the population casualty disaster-bearing body level of each slope unit.

[0012] Further, step S6 specifically includes: S61: obtaining the disaster-bearing body level based on the economic loss disaster-bearing body level and the population casualty disaster-bearing body level; S62: constructing a hazard-disaster-bearing body correction matrix based on the hazard-disaster-bearing body level; S63: generating the risk warning level for each slope unit based on the hazard-disaster-bearing body correction matrix.

[0013] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for early warning of meteorological risks of landslide disasters in the region.

[0014] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method for early warning of meteorological risks of regional landslide disasters.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for early warning of meteorological risks of regional landslide disasters.

[0016] The regional landslide disaster meteorological risk early warning method, storage medium, and computer equipment provided by this invention have the following beneficial effects: This invention addresses the shortcomings of existing technologies by fully considering the impact of disaster-bearing body factors on meteorological early warning of landslide disasters. Based on landslide hazard warnings combining rainfall thresholds and susceptibility levels, it further adjusts the warning results by coupling economic and population-based disaster-bearing body levels. By comprehensively considering the interaction of landslide susceptibility, rainfall factors, and disaster-bearing body levels, it achieves more accurate meteorological early warnings for landslide disasters, thereby effectively improving the accuracy of landslide disaster warnings. The results of this invention can provide insights for optimizing meteorological early warnings of landslide disasters and are of great significance for improving the accuracy of landslide disaster meteorological early warnings and reducing the losses caused by landslides. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a flowchart of the regional landslide disaster meteorological risk early warning method provided by the present invention; Figure 2 This is a schematic diagram illustrating the implementation of the regional landslide disaster meteorological risk early warning method provided by the present invention; Figure 3 This is a structural block diagram of the computer device provided by the present invention. Detailed Implementation

[0018] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] Figure 1 A schematic diagram of the regional landslide disaster meteorological risk early warning method according to this embodiment is shown. In this embodiment, the regional landslide disaster meteorological risk early warning method includes the following steps: S1: Based on geological environment data and historical landslide disaster data, slope units are obtained, and the slope units are evaluated using an information volume model to obtain the susceptibility level; In one exemplary embodiment, step S1 specifically includes: S11: Obtain geological environment data and historical landslide disaster data, including high-precision DEM data, soil and rock types, road distribution, river distribution, normalized vegetation index, etc. S12: Based on the high-precision DEM data, slope units are divided using township-level administrative boundaries; S13: Preprocess the geological environment data and historical landslide disaster data to obtain preprocessed geological environment data and historical landslide disaster data; In one exemplary embodiment, the preprocessing includes unifying the coordinate system, raster resampling, and handling missing values; S14: Based on the preprocessed geological environment data and historical landslide disaster data, use the Pearson correlation coefficient to perform correlation analysis on each indicator, remove indicators with large absolute values ​​of correlation coefficients, and use the remaining indicators as landslide susceptibility evaluation factors. S15: Calculate the information content of evaluation factors for landslide susceptibility using the information content model; In one exemplary embodiment, step S15 specifically includes: calculating the information content of the landslide susceptibility evaluation factors using an information content model, as shown in the formula: , in, for The amount of information in the index The landslide occurred in the first The number within the category indicator This represents the total number of landslides. For the first The area of ​​the index region, The total area of ​​the study area; S16: Based on the information content of the landslide susceptibility evaluation factors, obtain the total information content value; S17: Based on the total value of the information and the spatial distribution of historical landslide disaster points, the region is divided into four levels: non-prone, low-prone, medium-prone, and high-prone; the mode of the probability level within each slope unit is counted as the probability level of the corresponding slope unit. S2: Based on hourly rainfall data that induces rainfall-induced landslide disasters, a rainfall threshold model is constructed, which includes a threshold curve and a critical rainfall threshold; In one exemplary embodiment, step S2 specifically includes: S21: Filter landslide disaster sites induced by rainfall and collect historical rainfall information for each landslide disaster site; S22: Based on the historical rainfall information of each landslide disaster site, determine the number of effective rainfall days in the early stage by statistically analyzing the correlation between the number of landslide events and the cumulative rainfall on different days after the landslide. S23: Based on the number of days of effective rainfall in the previous period, determine the rainfall attenuation coefficient using the Pearson correlation coefficient, and calculate the effective rainfall in the previous period; In one exemplary embodiment, step S23 specifically includes: determining the rainfall attenuation coefficient based on the Pearson correlation coefficient according to the number of days of effective rainfall in the preceding period, and calculating the effective rainfall in the preceding period, as shown in the formula: , in, This represents the effective rainfall in the preceding period; The rainfall on the day of the landslide disaster; Before the landslide disaster occurred Daily rainfall; This is the rainfall attenuation coefficient; This refers to the number of days of effective rainfall in the preceding period; S24: Fit the relationship curve between the previous effective rainfall and the current rainfall, classify the warning level according to the probability of landslide disaster, determine the threshold curve and critical rainfall threshold under different warning levels, and obtain the rainfall threshold model; In one exemplary embodiment, the threshold curve is as follows: , in, This represents the effective rainfall in the preceding period; The rainfall on the day of the landslide disaster; For statistical parameters, A constant that is greater than or equal to 0; S3: Based on the aforementioned susceptibility level and rainfall threshold model, the hazard warning level for each slope unit is obtained; In one exemplary embodiment, step S3 specifically includes: S31: Determine the rainfall level based on the threshold curve and previous and forecast rainfall, and construct a landslide disaster meteorological hazard early warning matrix based on the susceptibility level and rainfall level; As an exemplary embodiment, in step S31, the rainfall level is determined by combining previous rainfall, forecast rainfall, and threshold curves. A landslide disaster meteorological hazard early warning matrix is ​​constructed by coupling the susceptibility classification and the rainfall level. The principle followed by this matrix is: the warning level of high hazard and above only occurs when the rainfall level and susceptibility level are high; as the rainfall warning level decreases, the regional warning level gradually decreases. S32: Based on the landslide disaster meteorological hazard warning matrix, obtain the hazard warning level for each slope unit; S4: Based on relevant economic data, construct an evaluation index for economic loss-bearing bodies and an economic loss-bearing body level for each slope unit; In one exemplary embodiment, the relevant economic data includes building attribute data, road attribute data, land use type, and regional GDP; The building attribute data includes the building type, area, height, and years of use; The road attribute data includes the road type and length; In one exemplary embodiment, step S4 specifically includes: S41: Based on relevant economic data, construct an evaluation index for the economic loss-bearing body; S42: The range method is used to standardize the evaluation index of the economic loss disaster-bearing body to obtain the standardized economic loss disaster-bearing body evaluation index. S43: Perform KMO test and Bartlett's sphericity test on the standardized economic loss disaster-bearing body evaluation index to obtain the tested economic loss disaster-bearing body evaluation index; S44: Based on the tested economic loss disaster-bearing body evaluation index, use factor analysis to extract factors with a cumulative variance contribution rate of more than a preset percentage, and calculate the corresponding factor score coefficient matrix, variance contribution rate, and factor score. In one exemplary embodiment, the preset percentage is 85%; S45: Using the variance contribution rate of each factor as a weight, the scores of each factor are weighted and summed to obtain the economic disaster-bearing body score of each slope unit. In one exemplary embodiment, step S45 specifically includes: weighting the scores of each factor by the variance contribution rate of each factor to obtain the economic disaster-bearing body score of each slope unit, as shown in the formula: , in, Scoring for economic disaster-bearing entities The number of disaster-bearing factors contributing to economic losses. For the first The variance contribution rate of each factor Scoring for each factor; S46: Based on the economic disaster-bearing body score, the economic loss disaster-bearing body of each slope unit is classified into levels using the quantile method to obtain the economic loss disaster-bearing body level of each slope unit. In one exemplary embodiment, the economic loss disaster-bearing body level includes low risk, medium risk, and high risk; S5: Based on relevant population data, construct population casualty disaster-bearing body indicators and population casualty disaster-bearing body levels for each slope unit; In one exemplary embodiment, the relevant population data includes population density, age group distribution, gender, and education level; In one exemplary embodiment, step S5 specifically includes: S51: Construct a population casualty disaster bearing capacity index based on relevant population data; S52: The range method is used to standardize the population casualty disaster-bearing body index to obtain the standardized population casualty disaster-bearing body index. S53: Construct a judgment matrix using the analytic hierarchy process (AHP) and calculate the weights of each indicator; S54: Based on the weights of each indicator and the standardized population casualty disaster-bearing body indicator, calculate the comprehensive index of population casualty disaster-bearing body using the weighted summation method; In one exemplary embodiment, step S54 specifically includes: calculating the comprehensive index of population casualty risk tolerance using a weighted summation method based on the weights of each indicator and the standardized population casualty risk tolerance index, as shown in the formula: , in, The comprehensive index of the population's casualty and disaster-bearing capacity. The number of disaster-bearing factors affecting population casualties. For each standardized population casualty disaster-bearing body indicator. The weights of the corresponding indicators; S55: Based on the comprehensive index of population casualty disaster-bearing body, the population casualty disaster-bearing body of each slope unit is classified into levels using the quantile method to obtain the population casualty disaster-bearing body level of each slope unit. In one exemplary embodiment, the population casualty disaster-bearing body level includes low risk, medium risk, and high risk; S6: Based on the economic loss disaster-bearing body level, the population casualty disaster-bearing body level, and the danger warning level, generate the risk warning level for each slope unit; In one exemplary embodiment, step S6 specifically includes: S61: Based on the aforementioned disaster-bearing body level for economic losses and disaster-bearing body level for casualties, the disaster-bearing body level is obtained; As an exemplary embodiment, in step S61, the disaster-bearing level is generated by cross-combining the economic loss disaster-bearing level and the population casualty disaster-bearing level. The principle followed is: when the population loss disaster-bearing level is high, the final disaster-bearing level is at least high; if the economic casualty level is also high, the final level is upgraded to extremely high; when the population loss level is medium or low, the economic loss level plays a moderating role. S62: Based on the aforementioned hazard warning level and disaster-bearing body level, construct a hazard-disaster-bearing body correction matrix; As an exemplary embodiment, in step S62, a hazard-hazard-bearing body correction matrix is ​​constructed. The principle followed by this matrix is: extremely high hazard only occurs when both the hazard-bearing body level and the hazard level are at or above high risk; as the hazard-bearing body level increases, the moderating effect on hazard gradually increases. S63: Generate the risk warning level for each slope unit based on the hazard-disaster-bearing body correction matrix.

[0020] In some embodiments, the above-described method for early warning of meteorological risks of regional landslide disasters can also be implemented in the following ways.

[0021] like Figure 2 As shown, in this embodiment, the regional landslide disaster meteorological risk early warning method includes the following steps: Step S1: Collect geological environment data and historical landslide disaster data, divide the slope into units, and apply the information content model to evaluate the susceptibility level of the slope units. The specific steps are as follows: Step S11: Collect multi-source data within the study area. The selection of data can be adjusted according to the geological environmental characteristics of the specific study area, including high-precision DEM data, rock and soil types, river distribution, normalized vegetation index, landform types and other geological environmental data, as well as historical landslide event data. Step S12: Based on high-precision DEM data and combined with administrative division boundaries, slope units are divided; Step S13: Preprocess the collected data, including unifying the coordinate system, raster resampling, and handling missing values; Step S14: Use Pearson correlation coefficient to perform correlation analysis on each indicator, remove indicators with large absolute values ​​of correlation coefficient, and use the remaining indicators as evaluation factors for landslide susceptibility. Step S15: Calculate the information content of each evaluation factor based on the information content model. The formula is as follows:

[0022] in, The landslide occurred in the first The number within the category indicator This represents the total number of landslides. For the first Area of ​​the index region The total area of ​​the study area; Step S16: Superimpose the information values ​​of each evaluation factor to obtain the total information value of the region; Step S17: Combining the spatial distribution and total information value of historical landslide disaster points, the region is divided into non-prone areas, low-prone areas, medium-prone areas, and high-prone areas in ArcMap using the quantile method or the natural discontinuity method; Step S18: Calculate the mode of the susceptibility level within each slope unit, and use it as the susceptibility level of the corresponding slope unit.

[0023] Step S2: Obtain hourly rainfall data for rainfall-induced landslide disasters and construct a rainfall threshold model. The specific steps are as follows: Step S21: Filter landslide disaster sites induced by rainfall, and collect rainfall information for each landslide disaster site on the current day and the previous 15 days; Step S22: Record the rainfall on the day the landslide occurred as P1, the cumulative rainfall of the previous two days (i.e. the day the landslide occurred and the day before) as P2, and so on. Calculate the cumulative rainfall of the previous 15 days as the rainfall impact factor, and calculate the number of landslide events on the day as the landslide impact factor. By analyzing the correlation between the landslide impact factor and the rainfall impact factor, determine the number of effective rainfall days in the early stage. Step S23: Take the effective number of rainfall days determined in step S22, adjust the rainfall attenuation coefficient with a step size of 0.1, calculate the previous effective rainfall under different attenuation coefficient conditions, and calculate the correlation between the previous effective rainfall and the frequency of regional landslide disasters under different rainfall attenuation coefficients using the Pearson correlation coefficient method. Select the rainfall attenuation coefficient with the highest correlation coefficient value as the optimal parameter. The formula for calculating the effective rainfall in the early stage is as follows:

[0024] in, This represents the effective rainfall in the preceding period; The rainfall on the day of the landslide disaster; Before the landslide disaster occurred Daily rainfall; This is the rainfall attenuation coefficient; This refers to the number of days of effective rainfall in the preceding period; Step S24: Based on the probability of landslide occurrence, four rainfall warning levels are divided: low risk, medium risk, high risk, and extremely high risk. A scatter plot is created with the previous effective rainfall as the horizontal axis and the current day's rainfall as the vertical axis. A power-law threshold curve is fitted for each warning level using nonlinear regression, and the critical rainfall threshold for the corresponding level is calculated accordingly. The fitted power-law threshold curve is as follows:

[0025] in, This represents the effective rainfall in the preceding period; The rainfall on the day of the landslide disaster; For statistical parameters, A constant that is greater than or equal to 0.

[0026] The probability of landslide occurrence represents the proportion of all landslide events triggered under the corresponding rainfall conditions. This proportion can be adjusted based on the actual conditions of the study area. The classification of landslide occurrence probabilities is shown in Table 1. Table 1: Classification of Landslide Disaster Early Warning Levels

[0027] Step S3: Construct a hazard assessment matrix by combining the susceptibility level and rainfall level, and output the hazard warning level for each slope unit. The specific steps are as follows: Step S31: Determine the rainfall level by combining previous rainfall, forecast rainfall, and threshold curves. Couple the susceptibility classification and rainfall level to construct a landslide disaster meteorological hazard early warning matrix, as shown in Table 2. Table 2: Landslide Disaster Meteorological Hazard Early Warning Matrix

[0028] The principle followed by this matrix is ​​that high-risk and above warning levels only occur when the rainfall level and susceptibility level are high; as the rainfall warning level decreases, the regional warning level gradually decreases.

[0029] Step S32: Output the hazard warning level of each slope unit according to the hazard warning matrix.

[0030] Step S4: Collect data on building attributes, road attributes, land use types, regional GDP, etc., construct an evaluation index for economic loss disaster-bearing bodies, and obtain the economic loss disaster-bearing body level of each slope unit; Step S41: Collect data on building type, area, height, and service life; road type and length; land use type; and regional GDP to construct an economic disaster-bearing body evaluation index. Some of the indicators were preprocessed as follows: The building area within each slope unit is calculated separately, and the building density data is obtained by dividing the building area within each slope unit by the slope unit area. The road length within each slope unit is calculated separately, and the road density data is obtained by dividing the road length within each slope unit by the slope unit area. Building types are assigned values ​​in descending order: brick-wood structure, brick-concrete structure, reinforced concrete structure, and steel structure. Road types are assigned values ​​in descending order: national highway, provincial highway, county road, and township road. Land use types are assigned values ​​in descending order: urban construction land, agricultural land, forest and grassland, and other. Step S42: Standardize the data using the range method to eliminate the influence between different units of measurement. The formula for the range method is as follows:

[0031] in, , , , These represent the original value, maximum value, minimum value, and standardized value of a certain indicator, respectively. Step S43: Perform the KMO test and Bartlett's test of sphericity to verify the applicability of the data; Step S44: Based on factor analysis, extract factors with a cumulative variance contribution rate of 85% or more, and calculate the corresponding factor score coefficient matrix, factor variance contribution rate weight, and factor score. Step S45: Using the variance contribution rate of each factor as weight, the scores of each factor are weighted and summed to obtain the economic disaster-bearing body score of each slope unit, as shown in the following formula:

[0032] in, For the first The variance contribution rate of each factor These are the standardized scores for each factor. Step S46: Use the quantile method to classify the economic disaster-bearing capacity of each slope unit into three levels: low risk, medium risk, and high risk.

[0033] Step S5: Collect data such as population density, age distribution, gender, and education level to construct population casualty disaster bearing capacity index and obtain the population casualty disaster bearing capacity level of each slope unit; Step S51: Collect data on population density, age distribution, gender, and education level; calculate the proportion of the population aged 0-14, 15-64, and 65 and above to represent age distribution; and calculate the proportion of the population who have never attended school, those who graduated from high school or below, and those who graduated from high school or above to represent education level. Step S52: Standardize the data using the range method to eliminate the influence between different units of measurement. The formula for the range method is as follows:

[0034] in, , , , These represent the original value, maximum value, minimum value, and standardized value of a certain indicator, respectively. Step S53: Based on the analytic hierarchy process, construct a judgment matrix by comparing the selected indicators pairwise, determine the relative importance of each indicator, calculate the weight of each indicator using the root method, and verify consistency. Step S54: Using the weighted summation method, multiply the standardized data by the weights to obtain the population disaster-bearing capacity score for each slope unit, as shown in the following formula:

[0035] in, These are the standardized values ​​for each evaluation indicator. The weights are the corresponding evaluation indicators; Step S55: Use the quantile method to classify the population disaster-bearing capacity of each slope unit into three levels: low risk, medium risk, and high risk.

[0036] Step S6: Couple the landslide hazard risk level with the disaster-bearing body level to generate the risk warning level for each slope unit. The specific steps are as follows: Step S61: Combine the economic loss disaster-bearing body level with the population casualty disaster-bearing body level to generate the disaster-bearing body level as shown in Table 3: Table 3: Disaster-bearing body levels

[0037] The principles followed in determining the disaster-bearing capacity level are as follows: when the population loss disaster-bearing capacity level is high, the final disaster-bearing capacity level must be at least high; if the economic casualty level is also high, the final level is upgraded to extremely high; when the population loss level is medium or low, the economic loss level plays a moderating role.

[0038] Step S62: Extract the meteorological hazard level and the disaster-bearing body level of the landslide disaster, and construct the hazard-disaster-bearing body correction matrix as shown in Table 4: Table 4: Hazard-Responsible Body Correction Matrix

[0039] The principle followed by the hazard warning level correction matrix is: extremely high hazard only occurs when both the disaster-bearing body level and the hazard level are at or above high risk; as the disaster-bearing body level increases, the moderating effect on hazard gradually increases.

[0040] Step S63: Generate the risk warning level for each slope unit based on the hazard warning level correction matrix.

[0041] This embodiment provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the steps of the aforementioned method for early warning of meteorological risks of regional landslide disasters. The storage medium may be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium may also include combinations of the above-mentioned types of memory.

[0042] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the above-described method for early warning of meteorological risks of regional landslide disasters.

[0043] like Figure 3As shown, the computer device 120 may include: at least one processor 121, such as a central processing unit (CPU), at least one communication interface 123, memory 124, and at least one communication bus 122. The communication bus 122 is used to enable communication between these components. The communication interface 123 may include a display screen and a keyboard; optionally, the communication interface 123 may also include a standard wired interface or a wireless interface. The memory 124 may be high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory 124 may also be at least one storage device located remotely from the aforementioned processor 121. The memory 124 stores application programs, and the processor 121 calls the program code stored in the memory 124 to execute any of the aforementioned method steps. The communication bus 122 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus 122 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3The term 124 is represented by a single line, but this does not imply a single bus or a single type of bus. The memory 124 may include volatile memory, such as random-access memory (RAM); it may also include non-volatile memory, such as flash memory, hard disk drive (HDD), or solid-state drive (SSD); or it may include combinations of the above types of memory. The processor 121 may be a central processing unit (CPU), a network processor (NP), or a combination of a CPU and an NP. The processor 121 may further include hardware chips. These hardware chips may be application-specific integrated circuits (ASICs), programmable logic devices (PLDs), or combinations thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof. Optionally, the memory 124 is also used to store program instructions. The processor 121 can call the program instructions to implement the regional landslide disaster meteorological risk early warning method as described in this embodiment.

[0044] This embodiment provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for early warning of meteorological risks of regional landslide disasters.

[0045] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for early warning of meteorological risks of regional landslide disasters, characterized in that, Includes the following steps: S1: Based on geological environment data and historical landslide disaster data, slope units are obtained, and the slope units are evaluated using an information volume model to obtain the susceptibility level; S2: Based on hourly rainfall data that induces rainfall-induced landslide disasters, a rainfall threshold model is constructed, which includes a threshold curve and a critical rainfall threshold; S3: Based on the aforementioned susceptibility level and rainfall threshold model, the hazard warning level for each slope unit is obtained; S4: Based on relevant economic data, construct an evaluation index for economic loss-bearing bodies and an economic loss-bearing body level for each slope unit; S5: Based on relevant population data, construct population casualty disaster-bearing body indicators and population casualty disaster-bearing body levels for each slope unit; S6: Based on the economic loss disaster-bearing body level, the population casualty disaster-bearing body level, and the danger warning level, generate the risk warning level for each slope unit.

2. The method for early warning of meteorological risks of regional landslide disasters according to claim 1, characterized in that, Step S1 specifically includes: S11: Obtain geological environment data and historical landslide disaster data, including high-precision DEM data, soil and rock types, road distribution, river distribution, normalized vegetation index, etc. S12: Based on the high-precision DEM data, slope units are divided using township-level administrative boundaries; S13: Preprocess the geological environment data and historical landslide disaster data to obtain preprocessed geological environment data and historical landslide disaster data; S14: Based on the preprocessed geological environment data and historical landslide disaster data, use the Pearson correlation coefficient to perform correlation analysis on each indicator, remove indicators with large absolute values ​​of correlation coefficients, and use the remaining indicators as landslide susceptibility evaluation factors. S15: Calculate the information content of evaluation factors for landslide susceptibility using the information content model; S16: Based on the information content of the landslide susceptibility evaluation factors, obtain the total information content value; S17: Based on the total value of the information and the spatial distribution of historical landslide disaster points, the region is divided into four levels: non-prone, low-prone, medium-prone, and high-prone; the mode of the probability level within each slope unit is counted as the probability level of the corresponding slope unit.

3. The method for early warning of meteorological risks of regional landslide disasters according to claim 1, characterized in that, Step S2 specifically includes: S21: Filter landslide disaster sites induced by rainfall and collect historical rainfall information for each landslide disaster site; S22: Based on the historical rainfall information of each landslide disaster site, determine the number of effective rainfall days in the early stage by statistically analyzing the correlation between the number of landslide events and the cumulative rainfall on different days after the landslide. S23: Based on the number of days of effective rainfall in the previous period, determine the rainfall attenuation coefficient using the Pearson correlation coefficient, and calculate the effective rainfall in the previous period; S24: Fit the relationship curve between the previous effective rainfall and the current rainfall, classify the warning level according to the probability of landslide disaster, determine the threshold curve and critical rainfall threshold under different warning levels, and obtain the rainfall threshold model.

4. The method for early warning of meteorological risks of regional landslide disasters according to claim 1, characterized in that, Step S3 specifically includes: S31: Determine the rainfall level based on the threshold curve and previous and forecast rainfall, and construct a landslide disaster meteorological hazard early warning matrix based on the susceptibility level and rainfall level; S32: Based on the landslide disaster meteorological hazard warning matrix, obtain the hazard warning level of each slope unit.

5. The method for early warning of meteorological risks of regional landslide disasters according to claim 1, characterized in that, Step S4 specifically includes: S41: Based on relevant economic data, construct an evaluation index for the economic loss-bearing body; S42: The range method is used to standardize the evaluation index of the economic loss disaster-bearing body to obtain the standardized economic loss disaster-bearing body evaluation index. S43: Perform KMO test and Bartlett's sphericity test on the standardized economic loss disaster-bearing body evaluation index to obtain the tested economic loss disaster-bearing body evaluation index; S44: Based on the tested economic loss disaster-bearing body evaluation index, use factor analysis to extract factors with a cumulative variance contribution rate of more than a preset percentage, and calculate the corresponding factor score coefficient matrix, variance contribution rate, and factor score. S45: Using the variance contribution rate of each factor as a weight, the scores of each factor are weighted and summed to obtain the economic disaster-bearing body score of each slope unit. S46: Based on the economic disaster-bearing body score, the economic loss disaster-bearing body of each slope unit is classified into levels using the quantile method to obtain the economic loss disaster-bearing body level of each slope unit.

6. The method for early warning of meteorological risks of regional landslide disasters according to claim 1, characterized in that, Step S5 specifically includes: S51: Construct a population casualty disaster bearing capacity index based on relevant population data; S52: The range method is used to standardize the population casualty disaster-bearing body index to obtain the standardized population casualty disaster-bearing body index. S53: Construct a judgment matrix using the analytic hierarchy process (AHP) and calculate the weights of each indicator; S54: Based on the weights of each indicator and the standardized population casualty disaster-bearing body indicator, calculate the comprehensive index of population casualty disaster-bearing body using the weighted summation method; S55: Based on the comprehensive index of population casualty risk, the population casualty risk of each slope unit is classified into different levels using the quantile method to obtain the population casualty risk level of each slope unit.

7. The method for early warning of meteorological risks of regional landslide disasters according to claim 1, characterized in that, Step S6 specifically includes: S61: Based on the aforementioned disaster-bearing body level for economic losses and disaster-bearing body level for casualties, the disaster-bearing body level is obtained; S62: Based on the aforementioned hazard warning level and disaster-bearing body level, construct a hazard-disaster-bearing body correction matrix; S63: Generate the risk warning level for each slope unit based on the hazard-disaster-bearing body correction matrix.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the regional landslide disaster meteorological risk early warning method as described in any one of claims 1-7.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the regional landslide disaster meteorological risk early warning method as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the regional landslide disaster meteorological risk early warning method according to any one of claims 1-7.

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