Geological disaster susceptibility qualitative evaluation method based on slope and valley unit coupling

Abstract

The invention provides a geological disaster susceptibility evaluation method based on slope and valley unit coupling. The geological disaster susceptibility evaluation method comprises a slope geological disaster susceptibility evaluation method, a valley geological disaster susceptibility evaluation method and a coupling geological disaster susceptibility comprehensive evaluation method. The method comprises the following steps: (1) dividing a research area into a plurality of slope units by adopting man-machine interaction; (2) acquiring disaster-pregnant geological environment conditions of the slope unit; the slope geological disaster susceptibility and the valley geological disaster susceptibility are qualitatively evaluated, the comprehensive geological disaster susceptibility is obtained by coupling evaluation results of the slope geological disaster susceptibility and the valley geological disaster susceptibility, and the evaluation results are more practical.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster assessment, and in particular to a method for qualitatively assessing geological disaster susceptibility based on the coupling of slope and gully units. Background Art

[0002] Geological hazard susceptibility refers to the degree of susceptibility to geological hazards within a given region, as determined by specific hazard-prone geological environmental conditions. Predicting the spatial characteristics of hazard occurrence, studying the mechanisms of hazard occurrence, and delineating susceptibility zones within a specific region are effective means of proactive disaster prevention and mitigation. Through geological hazard surveys and zoning (1:100,000 scale), detailed geological hazard surveys (1:50,000 scale), and county-level geological hazard risk surveys and assessments (1:50,000 scale), China has identified approximately 1.28 million square kilometers of high-susceptibility areas, 2.79 million square kilometers of medium-susceptibility areas, and 3.1 million square kilometers of low-susceptibility areas. This has led to the development of a susceptibility evaluation system based on empirical, statistical, and machine learning models. Empirical models, such as the comprehensive index method and the analytic hierarchy process, are knowledge-driven evaluation methods that rely on the technical personnel's experience and knowledge structure, as well as their understanding of the patterns of geological hazard occurrence. Statistical models, such as the information content method, frequency ratio model, and weight of evidence model, are discrete data statistical methods that rely on sample quality and are difficult to account for nonlinear relationships among hazard-prone factors. The machine learning model is a kind of logical data statistical method, such as support vector machine, logistic regression, random forest, etc., which has high computational efficiency, but is greatly affected by parameter selection. In terms of evaluation units, grid units based on the same specifications and slope units with the same slope mechanical mechanism become the main units for susceptibility evaluation. Limited by the survey accuracy, the evaluation results of the above-mentioned evaluation method in small areas and large scales are often inconsistent with reality, and the results are difficult to meet the needs of refined management and control. Therefore, based on the refined survey results, constructing an evaluation model that conforms to reality and improving the accuracy of regional geological disaster susceptibility prediction is a technical problem that needs to be solved urgently by technicians in this field. Summary of the Invention

[0003] In order to make up for the shortcomings of the existing technology, the embodiments of the present application propose a qualitative evaluation method for geological disaster susceptibility based on the coupling of slope and valley units to solve the problems existing in the existing technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0005] The purpose of the present invention is to provide a geological hazard susceptibility assessment method that couples the evaluation results of slope and valley units, integrates the advantages of empirical models, statistical models and machine learning models, and makes the evaluation results more accurate in small areas and large scales.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] (1) The study area is divided into several slope units using the human-computer interaction method.

[0008] (2) Field investigation of slope units to obtain the disaster-prone geological environmental conditions of the slope units and extract disaster-prone factors.

[0009] (3) Analyze the factors that cause slope disasters. Use frequency ratio to analyze the sensitivity of the factors that cause disasters and classify the factors. Test the correlation between the factors. Use logistic regression to determine the weights of the key factors that cause disasters. Then assign values ​​to the factors and establish a slope geological disaster susceptibility evaluation system.

[0010] (4) Calculate the slope geological disaster susceptibility index and classify the susceptibility levels.

[0011] (5) Divide the valleys where debris flows may occur in the study area into several valley units.

[0012] (6) Field investigation of gully units to obtain the disaster-prone geological environment conditions of gully units and extract disaster-prone factors.

[0013] (7) Score the disaster risk factor indicators of the gully units and classify them into susceptibility levels.

[0014] (8) Based on the results of slope unit susceptibility evaluation and valley unit susceptibility evaluation, the susceptibility of geological hazards is comprehensively determined according to the principle of taking the highest level.

[0015] (9) Select geological hazards and potential danger points to verify the evaluation results.

[0016] Compared with the existing geological hazard susceptibility assessment methods, this method adopts more generalized evaluation factors, establishes a more practical evaluation system, couples the evaluation results of slope and valley units, integrates the advantages of empirical models, statistical models and machine learning models, and improves the accuracy of the evaluation results.

[0017] Preferably, in an example of the present invention, the human-computer interaction method is used to divide the slope units, based on ArcG IS software, including: automatically generating slope units through ridge lines and valley lines on the ArcGIS software platform, superimposing hazard-prone body data, superimposing comprehensive remote sensing images, topographic maps, and geological maps, manually eliminating flat areas, merging slope units with too small areas, dividing slope units according to administrative boundaries, optimizing the division of slope units of hazard-prone bodies, merging slope units with similar topography and landforms, similar geological environmental conditions, similar disaster mechanisms, and consistent hazard-prone bodies, and delineating slope units.

[0018] Preferably, in one example of the present invention, the disaster-prone geological environmental conditions of the slope unit include topography (the geomorphic unit where the slope is located, the slope surface morphology, the slope profile morphology, slope gradient, slope direction, and slope height), geological structure (faults and folds), engineering geological rock group (rock layer strength, rock mass integrity, cover layer type, cover layer thickness), slope structure (structural surface combination type), water (surface water, groundwater), and human engineering activities (cutting slopes to build houses, cutting slopes to build roads, and slope reclamation), which are 6 categories and 17 items. Preferably, in one embodiment of the present invention, the classification of disaster-prone factors includes: the landform unit where the slope is located is divided into high and medium mountainous areas, low mountain and hilly areas, river valley terrace areas, and low mountainous areas; the slope surface morphology is divided into convex, concave, straight, and composite shapes according to the typical tangent shape of parallel contour lines; the slope profile morphology is divided into convex, concave, straight, broken line, and stepped shapes according to the typical tangent shape of perpendicular contour lines; the slope gradient is divided into <10°, [10,20)°, [20,30)°, and [30,45)°. , ≥45°; slope height is divided into <50m, [50,100)m, [100,150)m, [150,200)m, ≥200m; slope direction is divided into north, northeast, east, southeast, south, southwest, west, northwest; geological structure is divided into extremely serious impact, serious impact, general impact, slight impact, and no impact according to the relationship between fault zone and fold core and slope; rock strength is divided into hard rock, relatively hard rock, relatively soft rock, soft rock, and extremely soft rock; rock integrity is divided into complete, relatively complete, and relatively broken The types of overburden are divided into landslide deposit, residual slope deposit, alluvial and flood deposit, and artificial fill; the thickness of overburden is divided into <1m, [1,2)m, [2,4)m, [4,8)m, and ≥8m; the slope structure is divided into forward slope, oblique slope, transverse slope, reverse slope, and massive structure according to the relationship between the inclination of the underlying bedrock and the slope direction; the surface water is divided into extremely serious impact, serious impact, general impact, slight impact, and no impact according to the water level fluctuation and erosion; the groundwater is divided into groundwater level and buried depth. The impact depth and soil moisture content are divided into extremely serious impact, serious impact, general impact, slight impact and no impact; cutting slopes for building houses are divided into extremely serious impact, serious impact, general impact, slight impact and no impact according to the height and length of the cut slope; cutting slopes for road construction are divided into extremely serious impact, serious impact, general impact, slight impact and no impact according to the height and length of the cut slope; slope reclamation is divided into <5%, [5%, 25%), [25%, 50%), [50%, 75%) and ≥75% according to the proportion of slope reclamation area.

[0019] Preferably, in one embodiment of the present invention, the frequency ratio analysis of the sensitivity of the disaster factor includes the number frequency ratio and the area frequency ratio, and the calculation formula is:

[0020]

[0021] In the number frequency ratio, F iis the number of slopes with geological hazards within a certain factor classification interval; F is the total number of slopes within a certain factor classification interval; C i The number of slopes in a factor classification interval; C is the total number of slopes in the study area. In the area frequency ratio, F i is the total area of ​​slopes with geological hazards within a certain factor classification interval; F is the total area of ​​slopes within a certain factor classification interval; C i The total area of ​​slopes in a certain factor classification interval; C is the total area of ​​slopes in the study area.

[0022] Preferably, in one embodiment of the present invention, logistic regression is used to determine the weights of key disaster-prone factors. In the logistic regression model, the dependent variable is whether a geological disaster occurs within a slope unit, and the independent variables are disaster-prone factors such as topography, geological structure, engineering geological rock formations, slope structure, water, and human engineering activities. The weights and values ​​are determined by analyzing the contribution of each key factor to the occurrence of geological disasters.

[0023] Preferably, in one embodiment of the present invention, the scoring of the disaster-prone factor indicators of the gully units and the classification of the susceptibility levels are performed with reference to the quantitative scoring table for debris flow susceptibility in Appendix G.1 of the "Specifications for Engineering Investigation of Debris Flow Disaster Prevention" DZ0220-2006.

[0024] Preferably, in one embodiment of the present invention, the geological hazard susceptibility is comprehensively determined according to the principle of the highest level, including comprehensively determining the regional geological hazard susceptibility level based on the evaluation of slope susceptibility and valley susceptibility according to the discriminant matrix and the principle of the highest level. The discriminant matrix is ​​as follows:

[0025]

[0026]

[0027] The selection of geological hazards and hidden danger points is used to verify the evaluation results, including statistical verification of geological hazards and hidden danger points and ROC curve verification. Statistical verification of geological hazards and hidden danger points: more than 90% of geological hazards and hidden danger points are within the designated high, medium and low prone areas, and more than 80% of geological hazards and hidden danger points are within the designated high and medium prone areas, indicating that the evaluation results are relatively accurate. ROC curve verification: The area under the ROC curve AUC is usually used as an objective quantitative evaluation indicator to measure the accuracy of model prediction. The closer the AUC value is to 1, the higher the model prediction accuracy. Generally, AUC ≥ 0.7 indicates that the evaluation results are relatively accurate.

[0028] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0029] 1. The slope unit division method adopted is a slope unit based on human-computer interaction optimization. Compared with grid units and machine-divided slope units, it fully considers the similarity of geological background, the similarity of geological disaster causes and disaster mechanisms, the consistency of disaster-bearing bodies, and the unity of administrative divisions. It analyzes the disaster-prone conditions for geological disasters from a macro perspective and is more in line with the actual disaster-prone environment and geological disaster prevention and control.

[0030] 2. The hazard-prone geological environment is based entirely on field survey results. Seventeen hazard-prone factors across six categories, including slope topography, geological structure, water, engineering geological rock formations, slope structure, and human engineering activities, are extracted from the slope hazard-prone conditions survey. The spatial morphological characteristics of the slope are fully considered, and the impact of structures such as faults and folds on slope stability is qualitatively evaluated. The impact of river erosion, undercutting, and water level fluctuations on slopes is qualitatively analyzed. Slope-cutting activities such as building and road construction are quantitatively evaluated. The engineering geological rock formations are decomposed into four factors: rock mass strength, rock mass integrity, overburden type, and overburden thickness. This factor is more accurate than traditional factors such as distance to structures, distance to rivers, and distance to roads. A coupled approach of empirical, statistical, and machine learning models is used to conduct sensitivity analysis, correlation testing, and weighting analysis of these hazard-prone factors. A slope hazard susceptibility assessment model for key hazard-prone factors is established, which has strong generalization capabilities across small and large scales.

[0031] 3. Based on the results of field investigations, the susceptibility of geological disasters in valleys where debris flows may occur was evaluated. The evaluation results of slope geological disaster susceptibility and valley geological disaster susceptibility level were integrated. The evaluation results are more in line with reality and can provide a more reliable decision-making basis for disaster prevention and mitigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Schematic diagram of the process of the present invention;

[0033] Figure 2 It is a slope unit map automatically divided based on ArcGIS;

[0034] Figure 3 It is a slope unit division diagram based on human-computer interaction;

[0035] Figure 4 It is a geological hazard susceptibility assessment map based on slope units;

[0036] Figure 5 This is a geological hazard susceptibility assessment map based on gully units;

[0037] Figure 6 A comprehensive geological hazard susceptibility assessment map based on slope and valley units;

[0038] Figure 7 This is a historical disaster point verification diagram for the evaluation results in the present invention;

[0039] Figure 8 This is the ROC curve verification diagram of the evaluation results in the present invention. DETAILED DESCRIPTION

[0040] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] The present invention proposes a qualitative evaluation method for geological hazard susceptibility based on the coupling of slope and valley units, which includes the following steps:

[0042] Step 1: The study area is divided into several slope units using the human-computer interaction method;

[0043] Step 2: Field survey of slope units to obtain the disaster-prone geological environment conditions of the slope units and extract disaster-prone factors;

[0044] Step 3: Analyze the sensitivity of various slope hazard-prone factors and classify the hazard-prone factors, test the factor correlation, determine the key hazard-prone factors and their weights, and assign values ​​to the hazard-prone factors to establish a slope geological hazard susceptibility evaluation system;

[0045] Step 4: Calculate the slope geological hazard susceptibility index and classify the susceptibility levels;

[0046] Step 5: divide the valleys where debris flows may occur in the study area into several valley units;

[0047] Step 6: Field survey of gully units to obtain disaster-prone geological environmental conditions and extract disaster-prone factors.

[0048] Step 7: Score the disaster-prone factor indicators of the gully units and classify the susceptibility levels according to the standards;

[0049] Step 8: Based on the slope unit susceptibility evaluation results and the valley unit susceptibility evaluation results, the geological hazard susceptibility is comprehensively determined according to the principle of taking the highest level;

[0050] Step 9: Select geological hazards and potential danger points to verify the evaluation results.

[0051] In the embodiment of the present invention, in step 1, the specific division method is as follows:

[0052] (1) Obtain DEM, remote sensing images, topographic maps, geological maps, administrative boundaries, and disaster-prone data for the study area;

[0053] (2) Based on the ArcGI S platform, run the Fill command to analyze the DEM of the study area and generate an elevation model without depressions;

[0054] (3) Based on the ArcGIS platform, run the FlowDirection command to analyze the depression-free elevation model and generate a flow direction grid map;

[0055] (4) Based on the ArcGIS platform, run the FlowAccumulation command to analyze the flow direction grid map and generate the flow grid map;

[0056] (5) Based on the ArcGIS platform, run the SetNull command to analyze the flow grid map and generate the river network grid map;

[0057] (6) Based on the ArcGIS platform, run the StreamLink command to analyze the river network raster map and generate the river connection raster map;

[0058] (7) Based on the ArcGIS platform, run the Watershed command to analyze the flow direction raster map and the river connection raster map to generate the watershed raster map;

[0059] (8) Based on the ArcGIS platform, run the Rastertopo lygon command to analyze the watershed raster map and generate a watershed vector map;

[0060] (9) Based on the ArcGIS platform, invert the elevation model and repeat steps 2-8 to obtain the watershed vector diagram of the inverse elevation model;

[0061] (10) Based on the ArcGIS platform, run the Merge command to analyze the watershed vector map and the inverse elevation model watershed vector map to obtain the machine-divided slope unit file.

[0062] (11) Overlay remote sensing images, topographic maps, geological maps, administrative boundaries, and hazard-prone volume data;

[0063] (12) Based on human-computer interaction, areas with flat terrain and extremely low possibility of geological disasters are manually eliminated;

[0064] (13) Based on human-computer interaction, merge slope units with too small areas;

[0065] (14) Based on human-computer interaction, the slope units that cross administrative boundaries are segmented according to administrative boundaries;

[0066] (15) Based on human-computer interaction, optimize and segment the slope units of the disaster-bearing body;

[0067] (16) Based on human-computer interaction, slope units with similar topography, similar geological environment conditions, similar disaster mechanisms, and consistent disaster-bearing bodies are merged;

[0068] (17) Form a slope unit based on human-computer interaction.

[0069] In this embodiment of the present invention, in step 2, based on field surveys, the geological environmental conditions that may lead to disasters on the slope are extracted, including 6 categories and 17 sub-items: topography (geomorphological unit where the slope is located, slope surface morphology, slope profile morphology, slope gradient, slope aspect, and slope height), geological structure (faults and folds), engineering geological rock formations (rock layer strength, rock mass integrity, overburden type, and overburden thickness), slope structure (structural surface combination type), water (surface water, groundwater), and human engineering activities (slope cutting for house construction, slope cutting for road construction, and slope reclamation). The classification of each geological environmental condition that may lead to disasters is shown in the following table:

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] In an embodiment of the present invention, in step 3, the frequency ratio is used to analyze the sensitivity of various disaster-prone factors of the slope. The slope surface morphology, profile morphology, slope, geological structure, rock layer strength, rock mass integrity, cover layer thickness, surface water, slope cutting for building, slope cutting for road construction, and slope reclamation have obvious control effects on the development of geological hazards. Logistic regression test is used for key disaster-prone factors to determine factor weights, and a slope geological hazard susceptibility evaluation system is established for the graded factors.

[0077]

[0078]

[0079] In the embodiment of the present invention, in step 4, the formula for calculating the slope geological disaster susceptibility index is:

[0080]

[0081] Y i is the susceptibility index of the i-th slope unit; X j is the jth factor weight; C jAssign a grade to the jth factor; n is the number of evaluation factor items.

[0082] In the embodiment of the present invention, in step 4, the slope geological disaster susceptibility level is divided into the following levels:

[0083]

[0084] In an embodiment of the present invention, in step 7, the hazard-prone factor indicators of the gully unit are scored and the susceptibility level is divided with reference to the "Specifications for Geological Hazard Risk Investigation and Assessment (1:50000)" (DZ / T 0438-2023).

[0085]

[0086]

[0087] In the embodiment of the present invention, in step 8, based on the slope unit susceptibility evaluation results and the valley unit susceptibility evaluation results, the geological hazard susceptibility is comprehensively determined according to the principle of the highest level. The discriminant matrix is ​​as follows:

[0088]

[0089]

[0090] In this embodiment of the present invention, in step 9, geological hazards and potential hazards are selected to verify the evaluation results. Verification methods include historical disaster statistical verification and ROC curve testing. In the historical disaster verification, more than 90% of geological hazard points are generally within the designated high, medium, and low susceptibility zones, and more than 60% of geological hazard points are generally within the designated high and medium susceptibility zones. In the ROC curve verification, an AUC value greater than 0.7 indicates that the evaluation conclusion is reliable.

[0091] In this example, Xuanwo Town in Hanyin County was used as a research example and the study area was divided into 996 slope units. Field data was collected for 17 slope hazard-prone factors across six categories: slope topography, geological structure, water, engineering geological formations, slope structure, and human engineering activities. A coupled empirical, statistical, and machine learning model was used to conduct sensitivity analysis, correlation testing, and weight analysis of these factors. A slope hazard susceptibility assessment model for key hazard-prone factors was established, and a susceptibility assessment was conducted.

[0092] In the example of the present invention, the study area is divided into 60 gully units, and 15 debris flow disaster-predisposing factors are obtained in the field, including the severity of collapse and landslides and soil erosion (natural and man-made) in the gully basin, the ratio of sediment supply length along the way, debris flow accumulation activity at the gully mouth, the longitudinal slope of the gully, the degree of regional tectonic influence, the vegetation coverage rate in the basin, the recent amplitude of the gully, the lithological influence, the storage of loose materials along the gully, the slope of the gully bank, the cross-section of the gully in the sand-producing area, the average thickness, the basin area, the relative height difference, and the degree of gully blockage. The disaster-predisposing factor indicators of the gully units are scored and classified into susceptibility levels with reference to the "Specifications for Geological Hazard Risk Investigation and Assessment (1:50000)" (DZ / T 0438-2023).

[0093] Based on the slope and valley unit susceptibility assessment results, a comprehensive assessment of geological hazard susceptibility was conducted using the principle of prioritizing the highest hazard susceptibility. The assessment results were then validated. Historical disaster statistics confirmed that 80.0% of geological hazard sites fell within the designated high and medium susceptibility zones, and 100.0% fell within the designated high, medium, and low susceptibility zones. The receiver operating characteristic (ROC) curve model validation accuracy (AUC) was 0.884.

[0094] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations that come within the meaning and range of equivalents of the claims be embraced therein.

[0095] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment have also been appropriately combined to form other implementation methods that are easy for those skilled in the art to understand.

Claims

1. A qualitative evaluation method for geological disaster susceptibility based on the coupling of slope and valley units, characterized by: The following steps are involved: Step 1: Divide the study area into several slope units using human-computer interaction; Step 2, obtaining the disaster-prone geological environment conditions of the slope unit; Step 3: Analyze the factors that cause slope disasters; according to known geological disasters and potential hazards, use the frequency ratio method to analyze the contribution rate of the factors that cause disasters and classify the factors that cause disasters; Logistic regression model was used to test the correlation of factors, determine the weights of key disaster-prone factors, and grade and assign values ​​to the disaster-prone factors to establish a slope geological disaster susceptibility evaluation system. Step 4: Score each key disaster-prone factor of each slope unit to obtain the slope geological disaster susceptibility index and classify the susceptibility level; Step 5: Divide the valleys where debris flow may occur in the study area into several valley units, which are formed by merging the slope units in the watershed; Step 6: Obtain disaster-prone geological environmental conditions of gully units, including 15 indicators: severity of collapse and landslides and soil erosion in the gully, ratio of sediment supply length along the way, debris flow accumulation activity at the gully mouth, longitudinal slope of the gully, degree of regional structural influence, vegetation coverage in the basin, recent amplitude of the gully, lithological influence, storage of loose materials along the gully, slope of the gully bank, cross section of the gully in the sand-producing area, average thickness, basin area, relative height difference, and degree of gully blockage; Step 7: Score the disaster-prone factor indicators of the gully units and classify them into susceptibility levels; Step 8: Based on the slope unit susceptibility evaluation results and the valley unit susceptibility evaluation results, the susceptibility of geological hazards is comprehensively determined according to the principle of the highest level; Step 9: Select geological hazards and potential danger points to verify the evaluation results.

2. The method for qualitatively evaluating geological disaster susceptibility based on the coupling of slope and valley units according to claim 1 is characterized in that: The human-computer interactive slope unit division method described in step 1 specifically includes the following steps: automatically generating slope units using ArcGIS software → superimposing disaster-prone body data → superimposing comprehensive remote sensing images, topographic maps, and geological maps → administrative division boundaries → manually correcting slope units → delineating slope units.

3. The method for qualitatively evaluating geological disaster susceptibility based on the coupling of slope and valley units according to claim 2 is characterized in that: The artificially modified slope units specifically include: eliminating flat areas, merging slope units with too small areas, dividing slope units by administrative boundaries, modifying and dividing slope units of disaster-prone bodies, and merging slope units with similar topography, similar geological environmental conditions, similar disaster mechanisms, and consistent disaster-prone bodies.

4. The method for qualitatively evaluating geological disaster susceptibility based on the coupling of slope and valley units according to claim 2 is characterized in that: The disaster-prone geological environmental conditions of the slope unit described in step 2 are obtained through field investigation, including six categories: topography, geological structure, engineering geological rock formations, slope structure, water and human engineering activities.

5. The method for qualitatively evaluating geological disaster susceptibility based on the coupling of slope and valley units according to claim 4 is characterized in that: The topography and geomorphology include the geomorphic unit in which the slope is located, the slope surface morphology, the slope profile morphology, slope gradient, slope direction and slope height; the geological structure includes faults and folds; the engineering geological rock group includes rock stratum strength, rock mass integrity, cover layer type and cover layer thickness; the slope structure is a structural surface combination type; the water includes surface water and groundwater; and human engineering activities include cutting slopes to build houses, cutting slopes to build roads, and slope reclamation.

6. The method for qualitatively evaluating geological disaster susceptibility based on the coupling of slope and valley units according to claim 1, characterized in that: The frequency ratio method in step 3 is divided into number frequency ratio and area frequency ratio, and its calculation formula is as follows: Among them, F i is the number of slopes with geological hazards within a certain factor classification interval; F is the total number of slopes within a certain factor classification interval; C i The number of slopes in a factor classification interval; C is the total number of slopes in the study area. In the area frequency ratio, F i is the total area of ​​slopes with geological hazards within a certain factor classification interval; F is the total area of ​​slopes within a certain factor classification interval; C i The total area of ​​slopes in a certain factor classification interval; C is the total area of ​​slopes in the study area; The calculation formula of the slope geological disaster susceptibility assessment model is: Among them, Y i is the susceptibility index of the i-th slope unit; X j is the jth factor weight; C j Assign a grade to the jth factor; n is the number of evaluation factor items.

7. The method for qualitatively evaluating geological disaster susceptibility based on the coupling of slope and valley units according to claim 1, characterized in that: The susceptibility of the gully unit in step 7 is calculated by referring to the debris flow susceptibility evaluation method, and the calculation formula is: Among them, Y i is the susceptibility value of the i-th valley unit; C j is the score of the jth factor; n is the number of evaluation factor items.

8. The method for qualitatively evaluating geological disaster susceptibility based on the coupling of slope and valley units according to claim 1, characterized in that: The susceptibility level is divided into four levels: non-susceptible, low-susceptible, medium-susceptible, and high-susceptible.

9. The method for qualitatively evaluating geological disaster susceptibility based on the coupling of slope and valley units according to claim 1, characterized in that: The verification is carried out through geological disaster and potential hazard point distribution ratio verification and ROC curve verification.

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