Rapid geological susceptibility evaluation method based on evaluation units with different scales

Abstract

The invention discloses a geological disaster susceptibility evaluation method based on evaluation units of different scales, and belongs to the technical field of hydrological and geological disasters. The problems that an existing geological disaster susceptibility evaluation method is insufficient in precision, and the efficiency needs to be improved are solved. The method comprises the following steps: firstly, preprocessing ground disaster factors, including grid value check, gradient and direction calculation and the like; then adding an information amount model algorithm in a Python environment, extracting classified grid information after re-classification to a disaster point attribute table, calculating a disaster point information amount value and assigning the value; and finally, carrying out overlay analysis on the ground disaster factors by utilizing a grid calculator of a GIS (Geographic Information System) to obtain a susceptibility evaluation map of grids and hydrological response units. According to the method, substances, energy and environmental factors of debris flow disasters are comprehensively considered, the evaluation precision and efficiency are improved by taking the hydrological response unit and the grid unit as partition units from the perspective of hydrology, a scientific and practical partition method is provided for geological disaster monitoring, and the method has important practical significance.

Description

Technical Field

[0001] The invention relates to the technical field of hydrology and geological disasters, and in particular to a method for rapid evaluation of geological susceptibility based on evaluation units of different scales. Background Art

[0002] By deeply analyzing the susceptibility of debris flows, that is, starting from the basic geological environmental conditions (internal control factors), evaluating the possibility of debris flows in a relatively stable disaster-prone environment and completing the susceptibility zoning, we can provide important decision-making support for the prediction and risk prevention of debris flow disasters.

[0003] The research on debris flow susceptibility assessment can be traced back to the 1970s, and initially relied mainly on subjective scoring by experts for judgment and evaluation. In the 1980s, scholars began to introduce mathematical statistics methods into the field of geosciences, and studied debris flow prediction from the perspective of disaster causation mechanism and mathematical physics principles, realizing the transition from qualitative analysis to quantitative analysis. In the 1990s, with the application and promotion of geographic information system (GIS) technology and high-precision geotechnical physics models, domestic and foreign researchers have achieved a series of representative and important results in debris flow disaster assessment.

[0004] Due to the complexity of the disaster-prone environment, there is still a lack of unified evaluation standards and technical specifications for the current debris flow disaster susceptibility assessment. With the continuous deepening and popularization of the debris flow evolution mechanism and disaster assessment zoning work in my country, the research on the susceptibility assessment zoning method for debris flow in areas with widespread debris flow distribution and dense population is particularly urgent and important. Summary of the invention

[0005] The purpose of the present invention is to provide a method for rapid evaluation of geological susceptibility based on evaluation units of different scales, which significantly improves the efficiency of the evaluation process. By introducing the ArcPy site package, its powerful geographic data processing capabilities are utilized to achieve automation and batch processing of various links in the evaluation of geological hazard susceptibility. By writing scripts to control the automated operation of GIS, the speed and accuracy of data processing are greatly improved. The present invention takes the Jinghe River Basin as the research object, aiming to solve the problems of complex operation, long time consumption and single evaluation unit in the traditional GIS interface operation in the basin susceptibility evaluation.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: a method for evaluating the susceptibility of geological disasters based on evaluation units of different scales, comprising the following steps:

[0007] Step 1, Data Preparation

[0008] Step 1.1 Data sources: prepare land use, soil type, digital elevation, slope, aspect, distance to river, multi-year average precipitation, terrain moisture index, normalized difference vegetation index and terrain relief. The land use, soil type, digital elevation, multi-year average precipitation and normalized difference vegetation index data are from the National Frozen Soil, Glacier and Desert Science Data Center (http: / / www.ncdc.ac.cn); slope, aspect, distance to river, terrain moisture, terrain relief and other data are extracted from digital elevation data.

[0009] Step 1.2 Data preprocessing: Use the digital elevation data in the terrain data to check whether there are any missing areas or grids; after the data is correct, extract the slope and aspect data from the digital elevation data;

[0010] In processing hydrological and meteorological data, the hydrological tools in the ArcGIS toolbox are used to perform depression filling, flow direction, and flow operations on the digital elevation data to extract the water system, select a suitable flow threshold to vectorize the raster river network, and manually adjust the river network offset to make the river network smoother.

[0011] The satellite images of Landsat8 OLI satellite are selected as the standard images of the present invention. In the process of selecting images, the map sheets with less than 5% cloud cover in the summer of June to September in the past five years are firstly screened out. Secondly, the downloaded satellite images are loaded into ENVI software and fused and cropped to convert them into satellite images of the study area. Then, the green band 3, the red band 4 and the near-infrared band 5 are fused to obtain the land use image of the study area.

[0012] Step 2: Create grid cells

[0013] Step 2.1, data reclassification: digital elevation data are divided into 5 categories using the natural breakpoint method, slope is divided into 5 categories using the manual breakpoint method, aspect is divided into 8 categories using the manual breakpoint method, distance to river data are divided into 5 categories using the buffer tool, multi-year average precipitation data are divided into 5 categories according to the manual breakpoint method, terrain moisture index is divided into 5 categories according to natural breakpoints, and normalized vegetation index is divided into 5 categories according to the natural breakpoint method.

[0014] Step 2.2, multi-value extraction of disaster points, load the disaster point layer into the map, use the multi-value extraction to point tool in the GIS toolbox to extract all the reclassified layers in step 2.1, use the bilinear interpolation method at the point location, and add the classification information of the reclassified layer where the point is located to the disaster point attribute table.

[0015] Step 2.3, information calculation

[0016] Step 2.3, information value calculation, based on the control factors of geological disaster-prone points, calculate the information of different evaluation factors and establish a susceptibility evaluation model; the larger the information value, the higher the possibility of geological disasters in the area; the information value L of each classification indicator of evaluation factor i i The calculation formula is:

[0017]

[0018] Where, L i is the information value of the ith evaluation factor disaster point, N is the total number of geological disaster points in the study area, N i is the number of geological disaster points that have occurred in the i-th evaluation factor classification index; A is the total number of grid units divided in the study area, A i is the total number of grid cells of the classification index of the i-th evaluation factor;

[0019] Determine whether the information value calculation results of each classification indicator meet the mathematical calculation standards. If not, assign it to 0; if it meets, assign it to the grid.

[0020] Step 2.4: Convert raster to surface and assign information value. Use the raster to surface tool to convert the reclassified layers of each evaluation factor in step 2.2 into surface vector files. Assign the information value of each evaluation factor obtained in step 2.3 to the corresponding surface vector file. Finally, convert the surface vector file of each factor into a raster file according to the information value field.

[0021] Step 3: Create a hydrological response unit

[0022] Step 3.1, set DEM, select DEM data from the map, select meter as the unit, and the unit size is 30m.

[0023] Step 3.2, generate sub-watersheds. In the Stream Definition tab, click Flow directions and accumulation. Then fill in the threshold in Area. Then click Create streams and outlets to create a new river network. Then select Whole watershed outlets in the Watershed outlets Selection and Definition tab. After confirming, click Delineate watershed to start generating sub-watersheds.

[0024] Step 3.3, calculate subbasin parameters. After completing steps 3.1 and 3.2, click Calculation of SubbasinParameters to calculate the subbasin parameters.

[0025] Step 3.4, establish the relationship between land use, soil type and index table. In the ArcSWAT toolbar, click Land Use / Soils / Slope Definition in HRUAnalysis to define land use, soil type and slope. First, load the land use layer into GIS, select the value of the land use layer to distinguish the land use type, and click OK to complete the assignment of the land use layer; then select the Soil Data tab to add the soil type layer to GIS, select the value of the soil type to distinguish the soil type, and click OK to complete the soil type attribute assignment.

[0026] Step 3.5, Slope Classification: Select Slope Discretization in the Slope tab, check Mulitple Slope, select “5” in Number of Slope Classes in Slope Classes, and click Reclassify to reclassify.

[0027] Step 3.6 Create the hydrological response unit. After completing steps 3.4 and 3.5, click the Overlay and Create HRUfeatures Class buttons to complete the overlay work and create the HRU hydrological response unit.

[0028] Step 4: Susceptibility Assessment

[0029] Step 4.1, based on the grid unit susceptibility zoning, when evaluating the susceptibility of the grid unit, add the raster layers of each evaluation factor and assign weights to them in the raster calculator in ArcGIS according to the results of step 2.4, superimpose the evaluation factors and reclassify them according to the natural breakpoint method, and finally obtain the susceptibility zoning map based on the grid unit

[0030] Step 4.2, based on the susceptibility zoning of the hydrological response unit, when the hydrological response unit is evaluated for susceptibility, the "zoning extraction" tool is used to calculate the results of step 2, enlarge the evaluation scale, extract the raster values ​​into the hydrological response unit in step 3.6, then assign weights to the evaluation factors, and use the raster calculator to overlay the evaluation factors, and finally obtain the susceptibility evaluation zoning map based on the hydrological response unit.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. The basic data required by the present invention mainly include the digital elevation model (DEM), slope, aspect, distance from the river, multi-year average precipitation (PRE), terrain wetness index (TWI), normalized difference vegetation index (NDVI), terrain relief (GI), land use, and land type of the study area, among which the slope, aspect, distance from the river, terrain wetness index, and terrain relief can all be processed by the digital elevation model. Other data can be easily obtained on the official website, which provides data quality assurance for the information model and GIS secondary development.

[0033] 2. The geological disaster susceptibility assessment method of the present invention is based on different evaluation units (grids, hydrological response units). The flow threshold of the water network is adjusted through the ArcPy site package to control the area and structure of the HRU. The grid values ​​of the study area are extracted into the hydrological response unit. The evaluation results are based on the hydrological response unit. The evaluation effect is more biased towards the hydrological mechanism. The susceptibility level boundaries are divided according to the boundaries of the hydrological response units of different susceptibility levels, which is more reasonable.

[0034] 3. The susceptibility evaluation method adopted by the present invention is an information volume model. In actual operation, it may be encountered that the calculation of disaster points and information volume values ​​does not conform to mathematical calculation rules. On this basis, the present invention adds judgment conditions to the calculation steps to avoid errors in raster information volume assignment.

[0035] 4. The operation process and technical method of the present invention are based on the ArcPy site package in the ArcGIS Pro platform. The evaluation process is simple and can be quickly used to make a susceptibility evaluation map of the study area, which is affected by the size of the study area. The evaluation results are highly accurate and can quickly and accurately complete the susceptibility evaluation level division at the grid and hydrological response unit scales.

[0036] 5. The present invention uses Python language combined with ArcPy library to realize the automatic evaluation of watershed disaster distribution. At the same time, the concept of hydrological response unit in SWAT hydrological model is introduced, and a new evaluation unit is proposed, thereby completing the task of watershed geological disaster assessment based on hydrological response unit and automatic evaluation technology.

[0037] 6. In the process of susceptibility assessment, the present invention adopts two evaluation scales based on raster and hydrological response unit. The combination of these two scales provides a more detailed and comprehensive perspective for the evaluation of geological disaster susceptibility. Through the automation function of ArcPy, large-scale GIS data can be processed quickly, including but not limited to tasks such as spatial analysis, data conversion and management. Such automated processing not only improves efficiency, but also reduces the possibility of human error and ensures the reliability of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1It is a technical roadmap of a rapid geological susceptibility evaluation method based on evaluation units of different scales in the present invention;

[0039] Figure 2 A watershed elevation classification map of a rapid geological susceptibility evaluation method based on evaluation units of different scales according to the present invention;

[0040] Figure 3 A watershed slope classification map of a rapid geological susceptibility evaluation method based on evaluation units of different scales according to the present invention;

[0041] Figure 4 A watershed slope classification map of a rapid geological susceptibility evaluation method based on evaluation units of different scales according to the present invention;

[0042] Figure 5 A classification diagram of the distance between a watershed and a river for a rapid evaluation method of geological susceptibility based on evaluation units of different scales according to the present invention;

[0043] Figure 6 A classification diagram of normalized index of watershed vegetation in a rapid evaluation method of geological susceptibility based on evaluation units of different scales according to the present invention;

[0044] Figure 7 A classification map of watershed topographic relief for a rapid geological susceptibility evaluation method based on evaluation units of different scales according to the present invention;

[0045] Figure 8 A classification map of watershed terrain humidity index for a rapid geological susceptibility evaluation method based on evaluation units of different scales according to the present invention;

[0046] Fig. 9 A multi-year precipitation classification map of a watershed based on a rapid geological susceptibility evaluation method of different scale evaluation units according to the present invention;

[0047] Fig.10 A watershed hydrological response unit division diagram of a method for rapid geological susceptibility evaluation based on evaluation units of different scales according to the present invention;

[0048] Fig.11 A grid-based susceptibility evaluation map of a geological susceptibility rapid evaluation method based on evaluation units of different scales according to the present invention;

[0049] Fig.12 The invention discloses a susceptibility evaluation diagram based on hydrological response units for a rapid geological susceptibility evaluation method based on evaluation units of different scales. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described implementation is only a part of the implementation of the present application, rather than all the implementations. Based on the implementation in the present application, all other implementations obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0051] This example takes the Jinghe River Basin as an example. The geological disaster susceptibility assessment is based on the grid and hydrological response unit and the information volume model is added. First, based on the DEM data, the slope, slope aspect, and terrain moisture index are extracted. Then, the natural breakpoint method is used for reclassification. Other factors are reclassified at the same time. The multi-value extraction to point tool in ArcGIS is used to extract the grid value into the disaster point attribute table. The information volume model is used to calculate the information volume value of each disaster point based on the number of disaster points, the grid area of ​​each level, the pixel size, and the total grid area. Then the information volume value is assigned to each grid. The grid calculator in GIS is used to overlay and analyze each factor and reclassify it according to the natural breakpoint method to finally obtain the zoning map of geological disaster susceptibility assessment. For the hydrological response unit, the three factors of land use, soil, and slope are combined. The internal underlying surface characteristics are relatively uniform and have similar hydrological characteristics. The information volume value obtained from the grid is extracted into the hydrological response unit and reclassified to obtain the susceptibility assessment zoning map based on the hydrological response unit.

[0052] See also Figure 1 , the process of the present invention is as follows:

[0053] 1. Data preparation

[0054] 1.1 Data Source

[0055] 1.1.1 Land use

[0056] The LC10-meter dataset of land use data in the Zhongjing River Basin (2020) of the National Frozen Soil, Glacier and Desert Science Data Center (http: / / www.ncdc.ac.cn) was used. This data was extracted from the land use data processed by the Sentinel-2 remote sensing images;

[0057] 1.1.2 Soil type

[0058] The 1:1 million soil map of the People's Republic of China was used, and the vector boundary of the Jinghe River Basin was used to cut out the study area. The soil types in this data are divided into 20 categories, including alluvial soil, tidal soil, leached gray-brown, leached brown soil, new accumulation soil, gray-brown soil, brown soil, brown soil, mountain meadow, red clay, gray-brown soil, gray-calcium soil, light gray-calcium soil, gray desert soil, dark gray-brown soil, loess soil, black hemp soil, black loam soil, and clay-based black loam.

[0059] 1.1.3 Digital Elevation

[0060] The 30-meter resolution digital elevation model (DEM) data of the Jinghe River Basin in the National Frozen Soil, Glacier and Desert Science Data Center (http: / / www.ncdc.ac.cn) was used;

[0061] 1.1.5 Average precipitation over the years

[0062] The multi-year average precipitation data from the Institute of Geological Hazards was used, and the vector boundary of the Jinghe River Basin was used to cut out the study area.

[0063] 1.1.6 Normalized Difference Vegetation Index

[0064] The Landsat8 OLI satellite image of the Jinghe River Basin was used and processed using ENVI software.

[0065] 1.2 Data Preprocessing

[0066] The “Con” function in the “SpatialAnalyst” toolbox of ArcPy was called to modify the missing values ​​in the raster files of data elevation, satellite images, land use, soil type, etc. within the Jinghe River Basin to 0.

[0067] The digital elevation data was processed using the "Hydrological Analysis" toolbox in the "SpatialAnalyst" toolbox. The watershed was extracted according to the steps of filling depressions, flow direction, and flow rate. Based on the watershed area of ​​45,000 km 2 , select the flow threshold of 20000 to vectorize the river network, and finally obtain the water system vector file.

[0068] Input the basin dem data, use the Slope_3d function in ArcPy to get the basin slope raster, use the "Aspect_3d" function to get the basin aspect raster, use the basin area and slope of the water system generation process to calculate the terrain wetness index, and use the "Neighborhood Analysis" and "Focal Statistics" in the "SpatialAnalyst" toolbox to calculate the terrain relief.

[0069] 2. Create grid cells

[0070] 2.1 Reclassification

[0071] 2.1.1 Elevation

[0072] ArcPy was used to call the Reclassify function, and "VALUE" was used as the reclassification field. The classification method was "Nature Break", which was divided into five types: [0,1213m), [1213,1418m), [1418,1640m), [1640,1999m), and [1999,2898m). The elevation reclassification results are shown in Figure 2 .

[0073] 2.1.2 Slope

[0074] According to the result of step 1.2.3, ArcPy's Reclassify function is called, and "VALUE" is used as the reclassification field. The classification method adopts manual classification and is divided into 6 categories according to [0,10°), [10,20°), [20,30°), [30,40°), [40,50°), and [50,60°). The slope reclassification results are shown in Figure 3 ;

[0075] 2.1.3 Aspect

[0076] According to the result of step 1.2.3, ArcPy's Reclassify function is called, and "VALUE" is used as the reclassification field. The classification method is manual classification, which is divided into 8 categories according to east, southeast, south, southwest, west, northwest, north, and northeast. The slope reclassification results are shown in Figure 4 .

[0077] 2.1.4 Distance from the river (river_distance)

[0078] According to the results of step 1.2.2, the water system vector file is divided into 300, 600, 900, and 1200 meter intervals to create a water system buffer vector file. Then, the "Erase" function is used to select the 0-300, 300-600, 600-900, and 900-1200 meter layers. Then, the "Merge" function is used to merge and merge these five layers. The "Clip" function is used to clip the water system buffer vector file. Finally, the "PolygonToRaster" function is used to convert the water system vector file into a raster file. The classification results of the distance to the river are shown in Figure 1. Figure 5 .

[0079] 2.1.5 Normalized Difference Vegetation Index (NDVI)

[0080] ArcPy was used to call the Reclassify function, and “VALUE” was used as the reclassification field. The classification method was “Nature Break”, which was divided into 5 categories according to [-1,-0.0019), [-0.0019,-0.05), [-0.05,0.12), [0.12,0.2052), and [0.2055,0.555). The reclassification results of the normalized vegetation index are shown in Figure 6 .

[0081] 2.1.6 Terrain relief

[0082] According to the result of step 1.2.3, call ArcPy's Reclassify function, use "VALUE" as the reclassification field, and use the "Nature Break" classification method to divide it into 5 categories according to [0,1.026), [1.026,1.092), [1.092,1.211), [1.211,1.489), and [1.489,4.376). The reclassification results of terrain relief are shown in Figure 7 .

[0083] 2.1.7 Topographic Wetness Index (TWI)

[0084] According to the result of step 1.2.3, call ArcPy's Reclassify function, use "VALUE" as the reclassification field, and use the "Nature Break" classification method to divide it into 5 categories according to [2.13, 5.88), [5.88, 7.39), [7.39, 9.71), [9.71, 13.36), and [13.36, 27.91). The reclassification results of the terrain moisture index are shown in Figure 8 .

[0085] 2.1.8 Multi-year average precipitation (PRE)

[0086] ArcPy was used to call the Reclassify function, and "VALUE" was used as the reclassification field. The classification method was "Nature Break", which was divided into 5 categories according to [354.4, 431.4 mm), [431.4, 489.0 mm), [489.0-538.2 mm), [538.2, 580.2 mm), and [580.2, 660.6 mm). The results of the multi-year average precipitation reclassification are shown in the table. Fig. 9 .

[0087] 2.2 Extracting reclassified information to disaster point attribute table

[0088] ArcPy calls the "ExtractMultiValuesToPoints" function, and uses bilinear interpolation at the disaster point location for the factors such as elevation, distance from the river, slope, aspect, multi-year average precipitation, terrain moisture index, normalized vegetation index, and terrain relief in the reclassification step, and extracts the category of the reclassification factor raster where the disaster point is located into the disaster point attribute table.

[0089] 2.3 Information value calculation

[0090] ArcPy was used to call the “SelectLayerByAttribute_management” and “GetCount_management” functions to count the number of disaster points, and the “GetCount_management” and “row.getValue” functions were used to calculate the number of raster categories, the raster category area, and the total area of ​​the raster. Finally, the information value of each evaluation factor was calculated using the information model.

[0091] The information value L of each classification index of evaluation factor i i The calculation formula is:

[0092]

[0093] Where, L i is the information value of the ith evaluation factor disaster point, N is the total number of geological disaster points in the study area, N i is the number of geological disaster points that have occurred in the i-th evaluation factor classification index; A is the total number of grid units divided in the study area, A i is the total number of grid cells of the i-th evaluation factor classification index; 2.4 Information value grid

[0094] ArcPy was used to call the “RasterToPolygon_conversion” function to convert the reclassified raster files of each evaluation factor into vector files. The information value of each evaluation factor was added to the information value calculation results of 2.3 into each reclassified vector file using the “AddField_management” and “UpdateCursor” functions. Finally, the reclassified file was converted into a raster file according to the “Information” field using the “PolygonToRaster” function.

[0095] 3. Create Hydrological Response Unit

[0096] 3.1 Setting DEM calculation sub-basin parameters

[0097] Load the digital elevation data of the Jinghe River Basin in step 1 into ArcSWAT, select meter as the unit, and the cell size is 30m. Click Flow directions and accumulation in the Stream Definition tab, then enter the area threshold of 10000 in Area, then click Create streams and outlets to create a new river network, then select Whole watershed outlets in the Watershed outlets Selection and Definition tab, and click Delineate watershed to start generating subbasins. Click Calculation of Subbasin Parameters to calculate the parameters of the subbasin.

[0098] 3.2 Establishing the relationship between land use, soil type and index table

[0099] Load the results of steps 1.1.1 and 1.1.2 into the ArcSWAT toolbar, and click LandUse / Soils / Slope Definition in HRU Analysis to define land use, soil type, and slope. First, define the land use layer, select the value of the land use layer to distinguish the land use type, and click "OK" to complete the assignment of the land use layer; then define the soil type, select the SoilData tab to add the soil type layer to the GIS, select the value of the soil type to distinguish the soil type, and click OK to complete the soil type attribute assignment.

[0100] Load the result of step 1.1.3 into the ArcSWAT toolbar, select the Slope Discretization option in the Slope tab, check Multiple Slope, select "5" in the Number of Slope Classes in Slope Classes, and click Reclassify to reclassify.

[0101] 3.3 Create hydrological response units.

[0102] After completing step 3.2, click the Overlay and Create HRU features Class buttons to complete the overlay and create the HRU hydrological response unit. The results are shown in Fig.10 .

[0103] 4. Susceptibility Assessment

[0104] 4.1 Zoning based on grid cell susceptibility

[0105] Based on the result of step 2.4, the “RasterCalculator_sa” function in ArcPy was called to add elevation, slope, aspect, distance to the river, terrain relief, terrain moisture index, normalized difference vegetation index, and multi-year average precipitation raster data for overlay analysis and reclassify according to the “Nature Break” method. Finally, a susceptibility zoning map based on raster cells was obtained. The final results are shown in Fig.11 .

[0106] 4.2 Susceptibility zoning based on hydrological response units

[0107] 4.2.1 Raster value partition extraction

[0108] Based on the result of step 3.3, the custom function "cal_zonal_extract" in ArcPy is called to extract the raster values ​​of step 2.4 such as elevation, slope aspect, river distance, terrain relief, terrain wetness index, normalized vegetation index and multi-year average precipitation in batches into hydrological response units according to the "mean" method; 4.2.2 Susceptibility zoning

[0109] Based on the result of step 4.2.1, the “RasterCalculator_sa” function in ArcPy was called to add elevation, aspect, distance to the river, terrain relief, terrain wetness index, normalized difference vegetation index, and multi-year average precipitation raster data for overlay analysis and reclassify according to the “Nature Break” method. Finally, a susceptibility zoning map based on hydrological response units was obtained. The final results are shown in Fig.12 .

[0110] It should be noted that the above embodiments do not limit the present invention in any way. For those skilled in the art, any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention. The protection scope required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for evaluating the susceptibility of geological hazards based on evaluation units of different scales, characterized in that: include: Data preparation: Obtain land use data, soil type data, digital elevation data, multi-year average precipitation data, and normalized difference vegetation index data; Extract slope, aspect, distance to river, terrain moisture, and terrain relief data from digital elevation data; Create grid cells: Reclassify the acquired data; Use the multi-value extraction tool to combine the disaster point layer with the reclassification layer to obtain the disaster point attributes; Calculate the information value of each evaluation factor and establish a susceptibility evaluation model; Convert the reclassified layer into a vector and assign information, then convert it into a raster file; Create a hydrological response unit: Set and configure digital elevation model data, generate sub-basins based on digital elevation model data, and calculate sub-basin parameters; Establish a definition of the relationship between land use, soil type and slope; Based on the above relationship definition, a hydrological response unit is created; Susceptibility Assessment: ArcPy is used to evaluate the susceptibility of geohazards based on grid cells, and the grid layers of each evaluation factor are superimposed. The corresponding weights are assigned according to the importance of each factor to obtain a zoning map of geological hazard susceptibility based on grid cells. ArcPy was used to perform susceptibility assessment based on hydrological response units. The zoning extraction tool was used to extract raster data into hydrological response units. The extracted evaluation factors were weighted and superimposed to obtain a zoning map for geological disaster susceptibility assessment based on hydrological response units.

2. The geological disaster susceptibility assessment method based on different scale assessment units according to claim 1 is characterized in that: The information value L i The calculation formula is: Where, L i is the information value of the ith evaluation factor disaster point, N is the total number of geological disaster points in the study area, N i is the number of geological disaster points that have occurred in the i-th evaluation factor classification index; A is the total number of grid units divided in the study area, A i is the total number of grid cells of the classification index of the i-th evaluation factor.

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