Method and system for identifying geological disaster-causing factors

Abstract

Embodiments of the present invention relate to a method and system for identifying geological hazard causative factors, comprising: obtaining geological hazard point data and environmental data for a target area; generating environmental impact factor data based on the environmental data; using a geographic detector to calculate and analyze the degree of influence of each environmental impact factor on the spatial distribution of geological hazard points based on the geological hazard point data and environmental impact factor data, thereby extracting hazard factors that affect the spatial distribution of geological hazard points; and mapping the target area, geological hazard type, and hazard factors to generate a hazard factor database for the target area. The embodiments of the present invention reduce the uncertainty of geological hazard susceptibility assessment by obtaining geological hazard point data and environmental data for different target areas and geological hazard types, using a geographic detector to extract hazard factors that affect the spatial distribution of geological hazards, and establishing a hazard factor database for different target areas and different types of geological hazards.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster risk assessment, and in particular to a method and system for identifying geological disaster hazard factors. Background Art

[0002] At present, the hazard factors in geological hazard susceptibility assessment mostly focus on topographic geological factors, and the indicators targeted are single. However, with global warming, the impact of climate change on geological hazards has increased, and the single indicator of hazard factors has led to a large degree of uncertainty in geological hazard susceptibility assessment.

[0003] How to extract corresponding disaster-causing factors for different disaster types and different regions in a targeted manner to reduce the uncertainty of geological disaster susceptibility assessment has become an urgent problem to be solved. Summary of the Invention

[0004] The purpose of the present invention is to address the defects of the existing technology and provide a method and system for identifying geological disaster hazard factors. By obtaining geological disaster point data and environmental data of different target areas and geological disaster types, using geographic detectors to extract the hazard factors that affect the spatial distribution of geological disasters, and establishing a hazard factor database for different target areas and different types of geological disasters, the uncertainty of geological disaster susceptibility assessment can be reduced.

[0005] To achieve the above-mentioned object, the present invention provides a method for identifying geological disaster hazard factors in a first aspect, comprising:

[0006] Obtain geological hazard point data and environmental data in the target area;

[0007] generating environmental impact factor data according to the environmental data;

[0008] Based on the geological hazard point data and environmental impact factor data, a geographic detector is used to calculate and analyze the degree of influence of each environmental impact factor on the spatial distribution of geological hazard points, and to extract the hazard-causing factors that affect the spatial distribution of geological hazards;

[0009] Data mapping is performed on the target area, geological hazard type and disaster-causing factors to generate a database of disaster-causing factors for the target area.

[0010] Furthermore, the geological disaster point data includes geological disaster type and spatial location data; the environmental data includes terrain data, soil data, geological data, hydrological data, land use data, vegetation data and climate data.

[0011] Furthermore, generating environmental impact factor data based on the environmental data specifically includes:

[0012] Calculating and generating slope factor data, aspect factor data, elevation variation coefficient factor data, terrain relief factor data, surface cutting depth factor data, and surface roughness factor data based on the elevation data of the terrain data;

[0013] Screening and generating soil bulk density factor data, soil texture factor data, gravel content factor data, soil thickness factor data, soil moisture factor data and soil erosion intensity factor data according to the soil data;

[0014] Calculating and generating distance factor data from the fault based on the fault data of the geological data; calculating and generating earthquake kernel density factor data based on the earthquake source location data and magnitude data of the geological data; and generating lithology factor data based on the lithology data set of the geological data;

[0015] The distance factor data from the river is calculated based on the river data of the hydrological data; the land use type factor data is generated based on the land use data; and the vegetation type factor data is generated based on the vegetation data.

[0016] Furthermore, generating environmental impact factor data based on the environmental data specifically includes:

[0017] The extreme temperature index data and the extreme precipitation index data are calculated and generated respectively based on the daily temperature and precipitation data of the climate data; the extreme temperature factor data and the extreme precipitation factor data are screened and generated based on the correlation and the difference in physical meaning between the extreme temperature index data and the extreme precipitation index data.

[0018] Furthermore, the extreme temperature factor data specifically include growing season length factor data, cold night number factor data, cold day number factor data, warm day number factor data, heat duration index factor data, and daily temperature difference factor data; the extreme precipitation factor data specifically include maximum 5-day precipitation factor data, continuous dry period factor data, continuous wet period factor data, heavy precipitation factor data, extremely heavy precipitation factor data, and annual total precipitation factor data.

[0019] Furthermore, after screening and generating the extreme temperature factor data and the extreme precipitation factor data based on the correlation and physical meaning difference between the extreme temperature index data and the extreme precipitation index data, the method further includes:

[0020] The extreme temperature factor data and the extreme precipitation factor data are spatially interpolated based on an interpolation algorithm to generate spatially continuous grid data of the extreme temperature factor data and spatially continuous grid data of the extreme precipitation factor data.

[0021] Furthermore, the interpolation algorithm is a TPSS interpolation algorithm.

[0022] Furthermore, the method further comprises:

[0023] The longitude and latitude in the terrain data are fixed as independent variables in the TPSS algorithm, and the altitude and distance from the coastline are set as independent variables or covariates to generate a variety of climate spatial interpolation methods;

[0024] TPSS spatial interpolation processing is performed on each climate spatial interpolation method, and the spline order, dependent variable conversion method and smoothing parameter are adjusted according to the error value to generate the optimal parameter configuration for each climate spatial interpolation method;

[0025] Compare multiple error statistical indicators of various climate spatial interpolation methods under optimal parameter configuration to generate the optimal spatial interpolation model.

[0026] Furthermore, the target area includes a first target area and a second target area. The first target area is a high-altitude mountainous area where the area above 3,000 meters above sea level accounts for no less than 80% of the total area. The second target area is an adjacent area of ​​mountainous areas and a transition zone between mountains and basins where the area above 3,000 meters above sea level accounts for less than 10% of the total area.

[0027] The second aspect of the present invention provides a geological disaster hazard factor identification system, the system includes an acquisition module, a data processing module, a hazard factor analysis module, and a hazard factor database construction module;

[0028] The acquisition module is used to acquire geological disaster point data and environmental data of the target area;

[0029] The data processing module is used to generate environmental impact factor data based on the environmental data;

[0030] The disaster factor analysis module is used to calculate and analyze the influence of each environmental impact factor on the spatial distribution of geological disaster points based on the geological disaster point data and the environmental impact factor data using a geographic detector, and extract the disaster factors that affect the spatial distribution of geological disasters;

[0031] The disaster factor database construction module is used to perform data mapping on the target area, geological disaster type and disaster factor to generate a target area disaster factor database.

[0032] The method and system for identifying geological disaster hazard factors provided by the embodiments of the present invention obtain geological disaster point data and environmental data for different target areas and geological disaster types, use geographic detectors to extract hazard factors that affect the spatial distribution of geological disasters, and establish a hazard factor database for different target areas and different types of geological disasters, thereby reducing the uncertainty of geological disaster susceptibility assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1A flow chart of a method for identifying geological disaster hazard factors provided in Example 1 of the present invention;

[0034] Figure 2 This is a system block diagram of a geological disaster hazard factor identification system provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0035] To make the objectives, technical solutions, and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings. It is apparent that the embodiments described are only some, not all, of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0036] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments.

[0037] The geological disaster hazard factor identification method and system provided by the embodiment of the present invention obtains geological disaster point data and environmental data of different target areas and geological disaster types, uses geographic detectors to extract the main hazard factors affecting the spatial distribution of geological disasters, and establishes a target area hazard factor database for different target areas and different types of geological disasters, thereby reducing the uncertainty of geological disaster susceptibility assessment. When conducting geological disaster susceptibility assessment in a new area, the corresponding hazard factors can be quickly extracted by searching the target area hazard factor database, thereby improving the assessment efficiency; by adjusting parameters and comparing multiple climate spatial interpolation methods, the optimal spatial interpolation model is obtained, which further reduces the uncertainty of geological disaster susceptibility assessment.

[0038] Example 1

[0039] Figure 1 This is a flow chart of the method for identifying geological disaster hazard factors provided in the first embodiment of the present invention. Figure 1 , the technical solution of the present invention is described with specific embodiments.

[0040] Step 110: Acquire geological disaster point data and environmental data of the target area.

[0041] Specifically, mountainous areas, due to their complex topography, are particularly prone to geological hazards. In one possible implementation, the target area includes a first target area and a second target area. The first target area is a high-altitude mountainous area where areas above 3,000 meters above sea level account for at least 80% of the total area. The second target area is adjacent to mountainous areas, where areas above 3,000 meters above sea level account for less than 10% of the total area, and the transition zone between mountainous areas and basins.

[0042] Among them, geological disaster point data include geological disaster types and spatial location data; environmental data include terrain data, soil data, geological data, hydrological data, land use data, vegetation data and climate data. Specifically, geological disaster types include landslides, debris flows and collapses. Terrain data include elevation data, which can be obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model dataset; soil data include soil texture, gravel content, bulk density and soil thickness, which can be obtained from the China High-Resolution National Soil Information Grid Basic Attributes Dataset, soil data also include soil moisture content, which can be obtained from China's 1 km resolution daily all-weather surface soil moisture dataset, soil data also include soil erosion intensity data, which can be obtained from the Pan-Level 3 65 National 1km Resolution Soil Erosion Intensity Dataset; geological data include fault data, earthquake data The main data sources are geological survey data, lithology data, water system data, vegetation type data and land use type data. Among them, fault data can be obtained from the 1:4 million active tectonic map of China, earthquake data can be obtained from the China Earthquake Networks Center, and earthquake data include source location, magnitude, time of occurrence and focal depth. Lithology data can be obtained from the global high-resolution lithology dataset; vegetation type data can be obtained from the 1:1 million vegetation type dataset of China; land use type data can be extracted from the multi-period land use remote sensing monitoring dataset of China; climate data include daily temperature and precipitation data, which can be obtained from the China Meteorological Administration.

[0043] Step 120: Generate environmental impact factor data based on the environmental data.

[0044] Specifically, slope factor data, aspect factor data, elevation variation coefficient factor data, terrain relief factor data, surface cutting depth factor data and surface roughness factor data are calculated and generated based on the elevation data of the terrain data.

[0045] Soil data was screened to generate soil bulk density factor data, soil texture factor data, gravel content factor data, soil thickness factor data, soil moisture factor data, and soil erosion intensity factor data. The soil bulk density factor data, soil texture factor data, and gravel content factor data all include data for six soil depths (0-5, 5-15, 15-30, 30-60, 60-100, and 100-200 cm). The soil texture factor data is based on the United States Department of Agriculture's soil texture classification standards, which classify soils into 12 texture categories based on the content of gravel (0.05-2 mm), silt (0.002-0.05 mm), and clay (<0.002 mm). Based on the soil erosion rate, soil erosion intensity is divided into slight erosion (<500 t·km -2 ·a -1 ), mild erosion (500-1000t·km -2 ·a-1 ), moderate erosion (1000-2000t·km -2 ·a -1 ), severe erosion (2000-4000t·km -2 ·a -1 ), extremely strong erosion (4000-8000t·km -2 ·a -1 ) and severe erosion (>8000t·km -2 ·a -1 ).

[0046] The distance factor data from the fault is calculated based on the fault data of the geological data; the seismic kernel density factor data is calculated based on the earthquake source location data and magnitude data of the geological data; the lithologic factor data is screened based on the lithologic data set of the geological data. Optionally, the extracted lithologic classification data is determined based on the preset proportion of the lithologic classification levels of the mapping units in the target area. For example, when the preset proportion of the categories is set to 90%, if the target area has 3412 mapping units, only 2286 mapping units have the secondary lithologic classification and 888 mapping units have the tertiary lithologic classification. The secondary lithologic classification and the tertiary lithologic classification account for only 68% and 20% respectively. Therefore, only the primary lithologic classification is used as the lithologic factor data.

[0047] The distance factor data from the river is calculated based on the river data of the hydrological data; the land use type factor data is generated based on the land use data; and the vegetation type factor data is generated based on the vegetation data.

[0048] The extreme temperature index and extreme precipitation index are calculated based on daily temperature and precipitation data from climate data. The extreme temperature factor and extreme precipitation factor data are generated by screening the extreme temperature index and extreme precipitation index data based on their correlation and physical differences. The indicators for the extreme temperature index and extreme precipitation index are selected from the indicators standardized and standardized by the World Meteorological Organization's Expert Group on Climate Change Detection and Indices for detecting and analyzing climate extremes. This index includes 27 extreme climate indices, including 16 extreme temperature indices and 11 extreme precipitation indices. To avoid redundancy and invalidity of explanatory variables, the extreme temperature factor and extreme precipitation factor data are screened based on the spatial correlation and physical differences of the 27 extreme climate indices. For diversity, it is preferred to include a variety of indices, including extreme value indices, extreme persistence indices, absolute threshold extreme indices, and relative threshold extreme indices.

[0049] In a specific example, the correlation values ​​of 16 extreme temperature indices and 11 extreme precipitation indices at 122 meteorological stations in and around the Hengduan Mountains were compared, and 6 extreme temperature factor data and 6 extreme precipitation factor data were screened out. The 6 extreme temperature factor data are the growing season length factor data, the number of cold nights factor data, the number of cold days factor data, the number of warm days factor data, the heat persistence index factor data and the temperature diurnal difference factor data. Except for the temperature diurnal difference factor data, the other 5 extreme temperature factor data are all indices describing the persistence of extreme temperatures, rather than temperature extreme value indices. Other temperature extreme value indices, such as the annual maximum daily maximum temperature, the annual maximum daily minimum temperature, the annual minimum daily maximum temperature and the annual maximum daily minimum temperature, are all used to describe the persistence of extreme temperatures. The spatial correlation between the daily minimum temperature and other extreme temperature indices is high. Compared with the temperature extreme value index, the continuous extreme temperature has a greater impact on the cryosphere activity. The six extreme precipitation factor data are the maximum 5-day precipitation factor data, the continuous dry period factor data, the continuous wet period factor data, the heavy precipitation factor data, the extremely heavy precipitation factor data and the annual total precipitation factor data. Among them, the maximum 5-day precipitation factor data is the extreme precipitation intensity index, and the continuous dry period factor data, the continuous wet period factor data, the heavy precipitation factor data, the extremely heavy precipitation factor data and the annual total precipitation factor data are the extreme precipitation index. Here, extreme precipitation intensity refers to the precipitation per unit time or a short period of time, and extreme precipitation refers to the total number of days exceeding a certain extreme precipitation threshold in a certain period of time.

[0050] In a possible embodiment, the environmental impact factor data for the above-mentioned target areas, namely the first target area and the second target area, may include slope factor data, aspect factor data, elevation variation coefficient factor data, terrain undulation factor data, surface cutting depth factor data, surface roughness factor data, soil bulk density factor data, soil texture factor data, gravel content factor data, soil thickness factor data, soil moisture factor data, soil erosion intensity factor data, distance from fault factor data, earthquake kernel density factor data, lithology factor data, distance from river factor data, land use type factor data, vegetation type factor data, growing season length factor data, cold night number factor data, cold day number factor data, warm day number factor data, heat persistence index factor data, daily temperature difference factor data, maximum 5-day precipitation factor data, continuous dry period factor data, continuous wet period factor data, heavy precipitation factor data, extremely heavy precipitation factor data and annual total precipitation factor data.

[0051] In an optional solution, after the extreme temperature factor data and the extreme precipitation factor data are screened and generated based on the correlation and physical meaning differences between the extreme temperature index data and the extreme precipitation index data, step 120 further includes:

[0052] Based on the interpolation algorithm, spatial interpolation processing is performed on the extreme temperature factor data and the extreme precipitation factor data to generate spatial continuous raster data of the extreme temperature factor data and the extreme precipitation factor data.

[0053] In a further optional solution, the interpolation algorithm is a Thin Plate Smoothing Spline (TPSS) interpolation algorithm. The specific processing steps of the TPSS interpolation algorithm include steps A1 to A3:

[0054] In step A1, the longitude and latitude in the terrain data are fixed as independent variables in the TPSS algorithm, and the altitude and distance from the coastline are set as independent variables or covariates to generate various climate spatial interpolation methods.

[0055] In one possible implementation, nine climate spatial interpolation methods, M1-M9, are generated, M1: independent variables are longitude and latitude, with no covariates; M2: independent variables are longitude and latitude, with the covariate being altitude; M3: independent variables are longitude and latitude, with the covariate being the distance from the coastline; M4: independent variables are longitude and latitude, with the covariate being the distance from the coastline and altitude; M5: independent variables are longitude, latitude, and altitude, with no covariates; M6: independent variables are longitude, latitude, and distance from the coastline, with no covariates; M7: independent variables are longitude, latitude, and altitude, with the covariate being the distance from the coastline; M8: independent variables are longitude, latitude, and distance from the coastline, with the covariate being altitude; M9: independent variables are longitude, latitude, distance from the coastline, and altitude, with no covariates.

[0056] In step A2, TPSS spatial interpolation processing is performed on each climate spatial interpolation method, and the spline order, dependent variable conversion method and smoothing parameter are adjusted according to the error value to generate the optimal parameter configuration for each climate spatial interpolation method.

[0057] Step A3: compare multiple error statistical indicators of various climate spatial interpolation methods under optimal parameter configuration to generate an optimal spatial interpolation model.

[0058] Specifically, the multiple statistical indicators may include the Pearson correlation coefficient, the coefficient of determination, the mean deviation, the mean absolute error, and the root mean square error. The Pearson correlation coefficient is used to measure the linear correlation between the observed and predicted values, the coefficient of determination is used to statistically calculate the approximation between the predicted and observed values, and the closer the Pearson correlation coefficient and the coefficient of determination are to 1, the higher the accuracy of the spatial interpolation model. The mean deviation is used to calculate the systematic deviation of the TPSS interpolation method, and the mean absolute error and the root mean square error are used to evaluate the magnitude of the average error.

[0059] In a specific example, the six extreme temperature factor data and the six extreme precipitation factor data in the Hengduan Mountains region, located in the first target area, were spatially interpolated using the nine climate spatial interpolation methods M1-M9. The spline order, dependent variable conversion method, and smoothing parameter were adjusted according to the error value, and multiple error indicators of the nine climate spatial interpolation methods under the optimal parameter configuration were compared to generate the optimal spatial interpolation model, as shown in Table 1 below:

[0060]

[0061]

[0062] Table 1

[0063] The TPSS interpolation algorithm provides two smoothing parameters: generalized cross-validation (GCV) and Gaussian Maximum Likelihood (GML). Selecting the optimal spatial interpolation model can improve the accuracy of hazard factor extraction.

[0064] Step 130: Based on the geological hazard point data and the environmental impact factor data, a geographic detector is used to calculate and analyze the influence of each environmental impact factor on the spatial distribution of the geological hazard point, and to extract the hazard factors that affect the spatial distribution of the geological hazard.

[0065] Specifically, the geological hazard point data are converted into hazard kernel density raster data, and the raster data of environmental impact factor data are reclassified into category data. The geographic detector is used to calculate and analyze the degree of explanation of the spatial distribution of geological hazard points by each environmental impact factor, and the factors are sorted by explanatory power. A preset number of environmental impact factors are selected as disaster-causing factors in order of explanatory power from large to small.

[0066] Geographic detectors are a set of statistical methods for detecting spatial heterogeneity and revealing the driving force behind it. Its core idea is based on the assumption that if an independent variable has an important influence on a dependent variable, then the spatial distribution of the independent variable and the dependent variable should be similar. Geographic detectors include four detectors: differentiation and factor detection, interaction detection, risk zone detection, and ecological detection. The present invention uses differentiation and factor detection, that is, detecting the spatial heterogeneity of the dependent variable Y and the extent to which an independent variable X explains the spatial heterogeneity of attribute Y. The spatial heterogeneity of Y and the explanatory power of X on Y can both be measured by q value, as shown in formula (1)-formula (2):

[0067]

[0068] Where h = 1, 2, ..., L is the number of layers of variable Y or factor X, that is, the number of categories or partitions; N h and N are the number of units in layer h and the whole area, respectively; and σ 2 where q is the variance of the Y values ​​for layer h and the entire region, respectively; SSW is the sum of the variances within the layer; and SST is the total variance for the entire region. The range of q is [0,1]. A larger q value indicates a greater spatial heterogeneity in Y. If the stratification is generated by the independent variable X, a larger q value indicates a greater explanatory power of the independent variable X for the dependent variable Y, and vice versa. In this paper, Y is the category of geological hazards, such as the spatial distribution of landslides, debris flows, and collapses; and X is the spatial distribution of various environmental factors.

[0069] Transforming the q value can satisfy the non-central F distribution, as shown in formula (3) and formula (4):

[0070]

[0071] Among them, λ is the noncentrality parameter, is the mean value of layer h. The significance of q value can be tested by looking up the table of λ. The larger the q value is, the greater the influence of environmental factors on the spatial distribution of geological hazard points.

[0072] In step 120, the spatial correlation of the screened extreme temperature index data and extreme precipitation index is small. Therefore, when the geographical detector is used to detect the differences in the explanatory power of each environmental factor data on the spatial distribution of geological disasters, the explanatory power of each factor is independent, that is, the difference in the number of explanatory factors does not affect the explanatory power of a certain factor on the spatial distribution of geological disasters.

[0073] In a specific example, disaster factors are extracted for target area A. The geological characteristics of area A are that the area with an altitude of more than 3,000 meters accounts for 83.3% of the total area, and the average altitude is 3,500-4,000 meters in the Hengduan Mountains and the Qinghai-Tibet Plateau. The top ten environmental factors with q values ​​are extracted in descending order as disaster factors. When the geological disaster type is landslide, the disaster factors are elevation variation coefficient factor data, elevation factor data, daily temperature difference factor data, warm day number factor data, continuous wet period factor data, terrain undulation factor data, growing season length factor data, soil bulk density factor data, surface cutting depth factor data, and maximum 5-day precipitation factor data. Local When the geological disaster type is debris flow, the disaster-causing factors are elevation variation coefficient factor data, terrain relief factor data, elevation factor data, continuous wet period factor data, warm day number factor data, surface cutting depth factor data, daily temperature difference factor data, soil bulk density factor data, growing season length factor data, and slope factor data; when the geological disaster type is collapse, the disaster-causing factors are continuous wet period factor data, earthquake kernel density factor data, daily temperature difference factor data, cold day number factor data, elevation variation coefficient factor data, warm day number factor data, continuous dry period factor data, terrain relief factor data, elevation factor data, and surface cutting depth factor data.

[0074] In another specific example, disaster factors are extracted for target area B. The geological characteristics of area B are that the area with an altitude of more than 3,000 meters accounts for less than 10% of the total area, and the southern section of the Hengduan Mountains and its adjacent areas with an average altitude of 2,000-2,500 meters. The top ten environmental factors with q values ​​are extracted in descending order as disaster factors. When the geological disaster type is landslide, the disaster factors are daily temperature difference factor data, cold night number factor data, heat persistence index factor data, elevation factor data, soil bulk density factor data, extreme precipitation factor data, distance from fault factor data, warm day number factor data, elevation variation coefficient factor data, and continuous wet period factor data. When the disaster type is debris flow, the disaster-causing factors are the maximum 5-day precipitation factor data, rock factor data, daily temperature difference factor data, earthquake kernel density factor data, soil texture factor data, cold day number factor data, cold night number factor data, heat duration index factor data, continuous wet period factor data, and distance from fault factor data; when the geological disaster type is collapse, the disaster-causing factors are the daily temperature difference factor data, heat duration index factor data, cold day number factor data, earthquake kernel density factor data, soil texture factor data, elevation variation coefficient factor data, cold night number factor data, gravel content factor data, continuous wet period factor data, and terrain relief factor data.

[0075] Step 140 : Mapping the target area, geological hazard type, and hazard factors to generate a hazard factor database for the target area.

[0076] Specifically, hazard factors are extracted from multiple target areas with typical characteristics. These target areas include geological features such as altitude range, average altitude range, longitude and latitude range, and geological hazard types such as landslides, debris flows, and collapses. To assess the susceptibility of a specific area to geological hazards, the hazard factor database for the target area is searched based on the area's geological characteristics and the type of geological hazard to be assessed. The corresponding hazard factors are then extracted to conduct a geological hazard susceptibility assessment for that area.

[0077] Example 2

[0078] Figure 2 This is a system block diagram of a geological disaster hazard factor identification system provided in the second embodiment of the present invention, such as Figure 2 As shown, the geological disaster hazard factor identification system 200 includes an acquisition module 201, a data processing module 202, a hazard factor analysis module 203, and a hazard factor database construction module 204.

[0079] The acquisition module 201 is used to acquire geological disaster point data and environmental data in the target area.

[0080] Among them, geological disaster point data includes geological disaster types and spatial location data; environmental data includes terrain data, soil data, geological data, hydrological data, land use data, vegetation data and climate data.

[0081] Among them, the target areas include the first target area and the second target area. The first target area is the high-altitude mountainous area where the area with an altitude of over 3,000 meters accounts for no less than 80% of the total area. The second target area is the adjacent areas of the mountainous areas and the transition zone between mountains and basins where the area with an altitude of over 3,000 meters accounts for less than 10% of the total area.

[0082] The data processing module 202 is used to generate environmental impact factor data according to environmental data.

[0083] Among them, the environmental impact factor data is generated based on the environmental data, including:

[0084] The slope factor data, aspect factor data, elevation variation coefficient factor data, terrain relief factor data, surface cutting depth factor data and surface roughness factor data are calculated based on the elevation data of the terrain data.

[0085] Based on the soil data screening, soil bulk density factor data, soil texture factor data, gravel content factor data, soil thickness factor data, soil moisture factor data and soil erosion intensity factor data are generated.

[0086] The distance factor data from the fault is calculated based on the fault data of the geological data; the earthquake kernel density factor data is calculated based on the earthquake source location data and magnitude data of the geological data; and the lithology factor data is generated by screening the lithology data set of the geological data.

[0087] The distance factor data from the river is calculated based on the river data of the hydrological data; the land use type factor data is generated based on the land use data; and the vegetation type factor data is generated based on the vegetation data.

[0088] The extreme temperature index data and extreme precipitation index data are calculated and generated based on the daily temperature and precipitation data of the climate data; the extreme temperature factor data and extreme precipitation factor data are screened and generated based on the correlation between the extreme temperature index data and the extreme precipitation index data and the difference in physical meaning.

[0089] Among them, the extreme temperature factor data specifically include the growing season length factor data, the number of cold nights factor data, the number of cold days factor data, the number of warm days factor data, the heat persistence index factor data, and the daily temperature difference factor data; the extreme precipitation factor data specifically include the maximum 5-day precipitation factor data, the continuous dry period factor data, the continuous wet period factor data, the heavy precipitation factor data, the extremely heavy precipitation factor data and the annual total precipitation factor data.

[0090] Furthermore, after screening and generating extreme temperature factor data and extreme precipitation factor data based on the correlation and physical meaning differences between the extreme temperature index data and the extreme precipitation index data, the data processing module 202 is also used to perform spatial interpolation processing on the extreme temperature factor data and the extreme precipitation factor data based on an interpolation algorithm to generate spatially continuous raster data of the extreme temperature factor data and spatially continuous raster data of the extreme precipitation factor data.

[0091] The interpolation algorithm is the TPSS interpolation algorithm.

[0092] Furthermore, the data processing module 202 is also used to fix the longitude and latitude in the terrain data as independent variables in the TPSS algorithm, set the altitude and the distance from the coastline as independent variables or covariates, and generate a variety of climate spatial interpolation methods; perform TPSS spatial interpolation processing on each climate spatial interpolation method, and adjust the spline order, dependent variable conversion method and smoothing parameter according to the error value to generate the optimal parameter configuration of each climate spatial interpolation method; compare multiple error statistical indicators of multiple climate spatial interpolation methods under the optimal parameter configuration, and generate an optimal spatial interpolation model.

[0093] The disaster factor analysis module 203 is used to calculate and analyze the influence of each environmental impact factor on the spatial distribution of geological disaster points based on geological disaster point data and environmental impact factor data using a geographic detector, and extract the disaster factors that affect the spatial distribution of geological disasters.

[0094] The disaster factor database construction module 204 is used to perform data mapping between the target area, geological hazard type and disaster factor to generate a target area disaster factor database.

[0095] The geological disaster hazard factor identification method of Example 1 is executed in the geological disaster hazard factor identification system of Example 2. The specific function of each module in the system is similar to the description of the above Example 1 and will not be repeated here.

[0096] Professionals should also be further aware that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0097] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0098] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for identifying geological disaster hazard factors, characterized in that: The method comprises: Acquire geological hazard point data and environmental data of the target area; the geological hazard point data includes geological hazard types; the environmental data includes topographic data, soil data, geological data, hydrological data, land use data, vegetation data and climate data; generating environmental impact factor data according to the environmental data; Based on the geological hazard point data and the environmental impact factor data, a geographic detector is used to calculate and analyze the degree of influence of each environmental impact factor on the spatial distribution of the geological hazard point, and the hazard factors affecting the spatial distribution of the geological hazard are extracted; wherein the geological hazard point data is converted into hazard kernel density raster data, the raster data of the environmental impact factor data is reclassified into category data, and the geographic detector is used to calculate and analyze the degree of explanation of the spatial distribution of the geological hazard point by each environmental impact factor, and the factors are sorted by explanatory power, and a preset number of environmental impact factors are selected as hazard factors in descending order of explanatory power; Carry out data mapping of target areas, geological hazard types and hazard factors to generate a hazard factor database for target areas; The step of generating environmental impact factor data based on the environmental data specifically includes: Screening and generating soil bulk density factor data, soil texture factor data, gravel content factor data, soil thickness factor data, soil moisture factor data and soil erosion intensity factor data according to the soil data; Calculate and generate river distance factor data based on the river data of the hydrological data; The extreme temperature index data and the extreme precipitation index data are calculated and generated respectively based on the daily temperature and precipitation data of the climate data; the extreme temperature factor data and the extreme precipitation factor data are screened and generated based on the correlation and the difference in physical meaning between the extreme temperature index data and the extreme precipitation index data.

2. The method for identifying geological disaster hazard factors according to claim 1, characterized in that: The geological hazard point data also includes spatial location data; the environmental data also includes terrain data, geological data, land use data, vegetation data and climate data.

3. The method for identifying geological disaster hazard factors according to claim 2, characterized in that: Generating environmental impact factor data according to the environmental data specifically includes: Calculating and generating slope factor data, aspect factor data, elevation variation coefficient factor data, terrain relief factor data, surface cutting depth factor data, and surface roughness factor data based on the elevation data of the terrain data; Calculating and generating distance factor data from the fault based on the fault data of the geological data; calculating and generating earthquake kernel density factor data based on the earthquake source location data and magnitude data of the geological data; and generating lithology factor data based on the lithology data set of the geological data; The land use data are screened to generate land use type factor data; and the vegetation data are screened to generate vegetation type factor data.

4. The method for identifying geological disaster hazard factors according to claim 1, characterized in that: The extreme temperature factor data specifically include growing season length factor data, cold night number factor data, cold day number factor data, warm day number factor data, heat duration index factor data, and daily temperature difference factor data; the extreme precipitation factor data specifically include maximum 5-day precipitation factor data, continuous dry period factor data, continuous wet period factor data, heavy precipitation factor data, extremely heavy precipitation factor data, and annual total precipitation factor data.

5. The method for identifying geological disaster hazard factors according to claim 1, characterized in that: After screening and generating the extreme temperature factor data and the extreme precipitation factor data based on the correlation and physical meaning difference between the extreme temperature index data and the extreme precipitation index data, the method further includes: The extreme temperature factor data and the extreme precipitation factor data are spatially interpolated based on an interpolation algorithm to generate spatially continuous grid data of the extreme temperature factor data and spatially continuous grid data of the extreme precipitation factor data.

6. The method for identifying geological disaster hazard factors according to claim 5, characterized in that: The interpolation algorithm is the TPSS interpolation algorithm.

7. The method for identifying geological disaster hazard factors according to claim 6, characterized in that: The method further comprises: The longitude and latitude in the terrain data are fixed as independent variables in the TPSS algorithm, and the altitude and distance from the coastline are set as independent variables or covariates to generate a variety of climate spatial interpolation methods; TPSS spatial interpolation processing is performed on each climate spatial interpolation method, and the spline order, dependent variable conversion method and smoothing parameter are adjusted according to the error value to generate the optimal parameter configuration for each climate spatial interpolation method; Compare multiple error statistical indicators of various climate spatial interpolation methods under optimal parameter configuration to generate the optimal spatial interpolation model.

8. The method for identifying geological disaster hazard factors according to claim 1, characterized in that: The target area includes a first target area and a second target area. The first target area is a high-altitude mountainous area where the area with an altitude of over 3,000 meters accounts for no less than 80% of the total area. The second target area is an adjacent area of ​​mountainous areas and a transition zone between mountains and basins where the area with an altitude of over 3,000 meters accounts for less than 10% of the total area.

9. A geological disaster hazard factor identification system, characterized in that: The system includes an acquisition module, a data processing module, a disaster factor analysis module, and a disaster factor database construction module; The acquisition module is used to acquire geological hazard point data and environmental data of the target area; the geological hazard point data includes geological hazard types; the environmental data includes topographic data, soil data, geological data, hydrological data, land use data, vegetation data and climate data; The data processing module is used to generate environmental impact factor data based on the environmental data; The hazard factor analysis module is used to calculate and analyze the influence of each environmental impact factor on the spatial distribution of the geological hazard point based on the geological hazard point data and the environmental impact factor data, and extract the hazard factors that affect the spatial distribution of the geological hazard; wherein the geological hazard point data is converted into hazard kernel density raster data, the raster data of the environmental impact factor data is reclassified into category data, and the geographic detector is used to calculate and analyze the degree of explanation of the spatial distribution of the geological hazard point by each environmental impact factor, and the factors are sorted by explanatory power, and a preset number of environmental impact factors are selected as hazard factors in descending order of explanatory power; The disaster factor database construction module is used to perform data mapping on the target area, geological hazard type and disaster factor to generate a target area disaster factor database; The data processing module generates environmental impact factor data based on the environmental data, specifically including: Screening and generating soil bulk density factor data, soil texture factor data, gravel content factor data, soil thickness factor data, soil moisture factor data and soil erosion intensity factor data according to the soil data; Calculate and generate river distance factor data based on the river data of the hydrological data; The extreme temperature index data and the extreme precipitation index data are calculated and generated respectively based on the daily temperature and precipitation data of the climate data; the extreme temperature factor data and the extreme precipitation factor data are screened and generated based on the correlation and the difference in physical meaning between the extreme temperature index data and the extreme precipitation index data.

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