Risk assessment of landslides induced by the Ms6.2 earthquake in Jishishan of Gansu province, China

Risk assessment of landslides induced by the Ms6.2 earthquake in Jishishan of Gansu province, China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Risk assessment of landslides induced by the Ms6.2 earthquake in Jishishan of Gansu province, China WANG HAO, Niu Quanfu, Cheng Xi'an, Wang Gang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4598625/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 2023-12-18T23:59, an earthquake measuring Ms6.2 occurred in Jishishan County, China, causing serious casualties and directly leading to the occurrence of a large number of landslides. After the earthquake, multiple aftershocks increased the risk of collapse and landslides. Based on high-resolution satellite images before and after the earthquake, a Maximum Entropy model was constructed using visually interpreted landslide points and impact factors characteristics to evaluate the risk of landslide disasters after the earthquake. The conclusions of the study are as follows: 1) The main distribution of earthquake-induced landslide disasters is in the elevation zone of 1800-2300m, on sunny slopes with a slope gradient of 20–25°, which are mostly developed in the area 1.5 km away from the roads, 1.7 km away from the fault zones, and 5 km away from the earthquake center. The majority of the landslide occurred in cropland and loam areas with higher population density in the earthquake region. 2) Based on the contribution rate and replacement importance of the impact factors, test gain value, AUC value, and regularized training gain value, the main impact factors for landslide risk induced by the earthquake were comprehensively determined as follows: Distance from the fault zone, Elevation, and Population density. 3) Based on the constructed Maximum Entropy model, it is found that there is a good consistency between the extremely high and high risk areas of landslide disasters in the earthquake zone and the seismic intensity. Among them, the extremely high and high risk areas are mainly distributed in the intensity zone VIII, with an area of 5.368km 2 , accounting for 77.82% of the total area of the extremely high and high-risk zones. The low and very low risk areas are mainly distributed in the intensity zones VI and VII, accounting for 92.80% of the total area of the study region. This paper constructs a Maximum Entropy model based on the analysis of the importance of impact factors to evaluate the risk of landslide disasters in the earthquake zone. The research results provide references for post-disaster reconstruction in the earthquake zone. Earth and environmental sciences/Natural hazards Health sciences/Risk factors Jishishan Ms6.2 earthquake landslide factors importance analysis MaxEnt model risk assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction At 23:59 on December 18, 2023, a magnitude 6.2 earthquake occurred in Liugou Township, Jishishan County, Linxia Prefecture, Gansu Province, with a focal depth of 10km( https://www.cenc.ac.cn ). As of 8:00 a.m. on the 22nd, the earthquake caused 117 deaths and 781 injuries in Gansu, and 34 deaths and 198 injuries in Qinghai. The earthquake was a thrust type earthquake with a maximum intensity of 8 degrees 1 . After the earthquake, a large number of mountains became unstable, collapsed and landslides occurred. The multiple aftershocks after the earthquake increased the risk of instability, collapse and landslides, which had a far-reaching influence on local residents. Earthquakes often trigger a large number of geological disasters such as collapse, landslides, and mud-rock flows, which seriously threaten the lives and property safety of local people 2 . Because China is located in the southeastern part of the Eurasian Plate, it is affected by the compression, collision and subduction of the Indian Plate and the Pacific Plate. The crust is deformed strongly and it is the most earthquake-active region in the world 3 . Especially on the eastern edge of the Qinghai-Tibet Plateau, the geological structure is complex, the topography is diverse, and there are dense fault zones. Geological disasters occur frequently, causing numerous casualties and huge property losses in the area 4,5 . For example, the Ms7.5 Diexi earthquake in Maoxian, Sichuan in 1933 caused a large number of landslides and collapses on both sides of the Minjiang River 6 . The Wenchuan earthquake on May 12, 2008 triggered no fewer than 48,000 landslides 7 , affecting an area of approximately 711.8 km 2 and causing more than 60,000 deaths 8 ; On April 14, 2010, the Yushu Ms7.1 earthquake triggered 2,036 landslides 9 , affecting an area of 1.194 km 2 and causing no less than 3,000 deaths 10 . on August 13, 2014, the Ludian Ms6.5 earthquake in Yunnan triggered 3,883 landslides 11 , affecting an area of 16.42 km 2 ; on August 8, 2017, the Jiuzhaigou Ms7.0 earthquake in Sichuan caused 25 deaths 12 ; and in 2022, the Luding Mw6.7 earthquake triggered 297 landslides, affecting an area of 1.5 km 2 13 . Compared with the losses caused by the Minxian Ms6.6 earthquake in 2013, the Jiuzhaigou Ms7.0 earthquake in 2017, and the Luding Ms6.9 earthquake in 2022, the losses caused by the Jishishan earthquake in Gansu on December 18, 2023 were greater 14 . According to data, geological disasters have occurred frequently in this area in history 15,16 . Therefore, conducting a geological risk assessment after the Jishishan earthquake is of practical significance for ensuring the safety of people’s property and lives. Since the 21st century, a large number of scholars have do research on the assessment of geological disaster risk. From the perspective of research methods, most of them are based on historical mountain torrent disasters and adopt empirical evaluation methods 17–20 , statistical analysis methods 21 or mathematical model methods 22–24 . Among them, the empirical method is easily affected by the subjective factors of the evaluator; the statistical analysis method requires a large number of flash flood disaster samples and predictive evaluation by analyzing the distribution patterns of the samples; although the mathematical model method is widely used because of its reliable evaluation results, its difficulty lies in the determination of the weights of disaster-causing factors. With the development of computers and machine learning algorithms, non-linear algorithms such as machine learning are introduced into the study of flood disasters, and the regularity of the training samples can be used to avoid the determination of the weight of the disaster factors. For example, Tan uses neural networks and GIS to conduct geological hazard risk assessment on Wanli District, Nanchang 25 . Ma uses six deep learning algorithms to evaluate the risk of geological disasters 26 . Jena uses convolutional neural networks to evaluate the risk of geological disasters in northern India 27 . XIE uses Bayesian optimization–support vector machine to conduct risk assessment of Nanping City 28 . Zhao uses machine learning algorithms to evaluate risk of risk of Hengduan Mountain 29 . Chen used random forest and radial basis function to conduct risk assessment of flood disasters in the Yangtze River Delta 30 . At present, geological risk assessment has become a hot topic in the field of disaster prevention and mitigation. Although the above algorithms have achieved good results, they did not consider the response, contribution rate and importance of influencing factor on risk assessment results, and Maxent solved this problem. Most studies are mainly focused on the characteristic analysis and risk assessment of impact factors and disasters such as landslides. There are fewer studies on the response analysis of impact factors to landslides, especially the contribution rate and importance analysis of influencing factors to the risk of disasters. To this end, this paper takes the Jishishan Ms6.2 earthquake area in Gansu as the test area. Based on multi-source remote sensing data, after in-depth discussion of the characteristic distribution between landslide risk and impact factors, as well as the response analysis of impact factors to landslides, a rapid assessment and analysis of the geological disaster risk in the earthquake area is carried out, in order to provide a reference for post-disaster rescue and reconstruction work. 2. Materials and Method 2.1.Study area The Ms6.2 earthquake in Jieshishan, Gansu, was located at the junction between Gansu and Qinghai provinces, with a center at 35.7°N-102.79°E and a depth of 10 km (Fig. 1 ). The earthquake affected 3 cities (states) and 9 counties (cities, districts) in Gansu Province, and 2 cities (states) and 4 counties (cities) in Qinghai Province.The earthquake area is a transition zone from the first step of the Tibetan Plateau to the second step of the Loess Plateau, with a large difference in elevation, the maximum difference being more than 3,000 meters, and strong activity of fault zones in the area, such as the south and north fault zones of the Laji Mountains, the Inverted Flowing River-Linxia Fault Zone, and the northern edge of the West Qinling Ridge, which is the area where earthquakes and other natural disasters are frequently occurring 31,32 . The earthquake caused a large number of casualties and property losses in the quake area, and also directly led to a large number of mountain destabilization, collapse and landslide disasters, and many aftershocks after the earthquake increased the risk of destabilization collapse and landslide. 2.2.Data 1) Satellite image data The optical remote sensing images used this time are Gaofen-1 and Jilin-1, with spatial resolutions of 2m and 0.75m respectively. The pre-earthquake images were taken on December 18, and the post-earthquake images were taken on December 20 and December 19 respectively. Image preprocessing mainly includes processing such as radiometric calibration, atmospheric correction, geometric correction and image fusion, and finally obtains true-color high-resolution images, which are mainly used for the visual interpretation of landslide points 33 . 2) Seismic point data The geographic locations of the earthquakes and aftershocks used were obtained from the China National Earthquake Data Center( https://search.asf.alaska.edu/#/ ). This data is for magnitudes greater than 3.0 or greater as of December 20, 2023 (Table 1 ). Table 1 Seismic site data No. Time of earthquake Longitude Longitude Depth (km) Magnitude (Ms) Location Note 1 2023-12-18T23:59:30.0 35°42′ 102°47′ 10 6.2 Jieshishan county epicentre 2 2023-12-19T00:24:49.9 35°44′ 102°47′ 10 3.9 Jieshishan county aftershock 3 2023-12-19T00:36:18.3 35°47′ 102°47′ 10 4.0 Jieshishan county aftershock 4 2023-12-19T00:43:12.9 35°47′ 102°46′ 10 3.4 Jieshishan county aftershock 5 2023-12-19T00:56:51.3 35°42′ 102°47′ 10 3.4 Jieshishan county aftershock 6 2023-12-19T00:59:11.3 35°44′ 102°46′ 10 3.1 Jieshishan county aftershock 7 2023-12-19T00:59:39.0 35°50′ 102°47′ 10 4.1 Jieshishan county aftershock 8 2023-12-19T01:10:31.4 35°48′ 102°47′ 10 3.2 Jieshishan county aftershock 9 2023-12-19T01:20:12.6 35°48′ 102°46′ 10 3.2 Jieshishan county aftershock 10 2023-12-19T02:10:06.4 35°50′ 102°46′ 10 3.2 Jieshishan county aftershock 11 2023-12-19T00:32:52.9 35°46′ 102°47′ 9 3.4 Jieshishan county aftershock 3) Impact factors data The impact factors data used in this paper mainly include topographical, fault zone, soil, road, populations, land cover and plant cover. 14 impact factors were obtained through data processing 20,21 (Table 2 ). Among them, 1) the topographic data is the ALOS digital elevation model (DEM) with a spatial resolution of 12.5 m, which is mainly used to obtain slope, aspect, sectional curvature, flat curvature, curvature, distance from rivers and topographic wetness index (TWI); 3) Road data, from the OSG ( Open Street Map) official website, used to calculate the distance from road; 4) Land use (LULC), which is the CLCD (China Land Cover Dataset) dataset of Wuhan University, with a spatial resolution of 30m, mainly including: corrpland, forest, shrub, grassland, water, snow/ice, barren and impervious; 5) Normalized Difference Vegetation Index (NDVI), from China National Tibetan Plateau Data Center, with a data source of MODIS and a spatial resolution of 250 m; 6) Population distribution, from WorldPop global population data, with a spatial resolution of 100 meters, downloaded from the GEE (Google Earth Engine) platform; 7) Soil texture data, from the World Soil Database, with a spatial resolution of 1 km. Table 2 Impact factors data Impact factors Data sources Topographical Elevation(m) DEM data downloaded from ASF website( https://search.asf.alaska.edu/#/ ), The other factors are DEM derived data Slope(º) Aspect Sectional curvature Flat curvature Curvature Distance from rivers(km) Topographic wetness index(TWI) Fault zone Distance from fault zones China National Earthquake Data Center( https://search.asf.alaska.edu/#/ ) Soil Soil texture data China National Cryosphere Deser Data Center( http://www.ncdc.ac.cn/ ) Road Distance from roads(km) OSM Official Website( https://www.openstreetmap.org/ ) Populations Population distribution(people/km 2 ) Land cover Land use Wuhan University CLCD dataset( https://zenodo.org/ ) Plant cover Normalized Difference Vegetation Index(NDVI) China National Tibetan Plateau Science Data Center( https://data.tpda.ac.cn/home ) 3. Method 3.1 Maximum Entropy model Maximum Entropy(MaxEnt) model is a method to predict the probability distribution of random events based on the principle of maximum entropy, which has been widely used in the fields of disaster risk evaluation and predicting species distribution 34 . The principle of geohazard prediction using the MaxEnt model is to establish a probabilistic model of geohazard risk based on the relationship between geohazard occurrences and impact factors, so as to predict the geohazard risk of the whole region. This paper takes 14 impact factors as constraints and landslide points as events to seek the maximum entropy of geological disaster risk under 14 constraints, and then risk assessment of landslide. 3.2 Data preprocessing The preprocessing of the 1205 landslide points data from visual interpretation includes 1) elimination of duplicated and erroneous points obtained from visual interpretation of landslides; 2) removal of auto-correlation points by establishing a fishing net; 3) data conversion, which mainly involves converting the final 980 landslide points data into csv format for model construction. Preprocessing of the impact factors, including resampling the factors to a uniform image scale, cropping so that all impact factors have the same row and column numbers, and then converting them to ASCII format was used to construct the MaxEnt model 35 . 3.3 Model Parameter Setting Model parameter tuning is an important part of building a MaxEnt model. The specific process includes, the output format is set to logistic, and the landslide points are divided into test set and training set, in which the proportion of random test set is set to 25% and 75% is used for model training 36 . Accuracy was assessed using ROC (Create response curves, ROC), with the Regularization Multiplier set to 1 and the number of repetitive modeling sessions set to 10, in order to prevent the occurrence of underfitting and overfitting phenomena and to optimize the model construction 37 . 3.4 Multilinear analysis Variance Inflation Factor (VIF) is a statistical measure of the severity of multicollinearity 38 . It indicates the extent to which the variance of a given independent variable is affected by multicollinearity. The calculation formula is shown below: where VIF is the value of the variance inflation factor of the impact factor X i , R 2 is the proportion of variance of the impact factor X i . VIF takes values from 1 to infinity. The closer the VIF value is to 1, the less the variance of the impact factor is affected by the other impact factors. A larger value of VIF indicates a stronger linear correlation between the independent variables, As shown in Table 3 . Table 3 VIF evaluation criteria VIF Value Covariance evaluation VIF = 1 No covariance 1 < VIF ≤ 5 Slight covariance 5 < VIF ≤ 10 Medium covariance 10 < VIF ≤ 100 Relatively strong covariance 100 < VIF Strong covariance 3.5 Risk evaluation For the classification of the grade of landslide risk evaluation results, with reference to the previous research results 39,40 and combined with the spatial distribution of landslide points obtained by visual interpretation of high resolution images, the grade of risky area is divided into five grades: extremely high risk zone, high risk zone, medium risk zone, low risk zone and very low risk zone. 3.6 Accuracy evaluation The ROC curve and AUC value (Area Under the ROC Curve are used in model accuracy evaluation as a common tool to assess the performance of binary classification models. The ROC curve describes the relationship between the sensitivity and specificity of the model at different thresholds, and the closer the ROC curve is to the upper left corner, the better the performance of the model 41 . The specific evaluation criteria for AUC values are shown in Table 4 . Table 4 AUC evaluation criteria AUC Value Precision Evaluation 0, 0.6) Very poor 0.6, 0.7) Poor 0.7, 0.8) General 0.8, 0.9) Good 0.9, 1 Very good 4. Result 4.1 Distribution characteristics of factors affecting landslide points This paper uses a visual interpretation of landslides by comparing Gaofen-1 and Jilin-1 satellite images before and after the Jieshishan Ms6.2 earthquake. Because there was a large amount of snowfall in the area before the earthquake, new soil was exposed in the area where the landslide occurred after the earthquake, which was conducive to the visual interpretation of satellite images of the landslide points. The principles of this visual interpretation are: Images with high spatial resolution in the preferred area are selected. If there are cloud cover or terrain shadows, images with similar time phases will be selected in order of spatial resolution from high to low, ultimately covering the entire earthquake zone. In the visual interpretation of landslide points, the main method used was to compare pre-earthquake and post-earthquake images. A total of 1,205 landslides and potential disaster points were catalogued in this interpretation (Fig. 2 ). Most of them were small collapses and landslides, mainly concentrated in the loess hilly areas on both sides of the Yellow River in the earthquake zone, near roads and valleys, and mostly developed on steep slopes of houses and roads 33 . The main risk-bearing objects threatened were roads and farmland. In order to further analyze the distribution characteristics of landslide points on various impact factors, the landslide points were superimposed on various factors in this study, and histograms were made for statistical analysis, where the horizontal axis was the classification of each factors and the vertical axis was the density. For each Topographical factors (Fig. 3 ),In the analysis, the elevation factor was divided into nine levels at intervals of 100 m. The superimposed statistical analysis showed that the earthquake-induced landslide points were basically parabolic in elevation factor distribution (R 2 = 0.7394), mainly distributed in the 1700–2250 m elevation zone; Regarding the distribution of landslide points on the slope, the slope was classified into 5° intervals. Statistics show that landslide points mainly occurred in the range of less than 7° and 20–25°, and were scattered in the range of slopes greater than 30°; On TWI, the occurrence of landslide points basically presents an exponential distribution (R2 = 0.6816); in terms of slope distribution, most of the earthquake-induced landslide disaster points occur in the east, southeast and south. For various distance factors (Fig. 4 ), the distance factor from the road was classified into buffer zones with equal intervals of 0.5 km. The analysis found that the vast majority of landslide points induced by earthquakes occurred within 1.5 km of the road; For the distance from the fault zone, the first level is 1 km, and the second level and above are 2 km. The analysis shows that the earthquake-induced landslide points basically present an exponential distribution in the distance from the fault zone (R 2 = 0.201), mainly distributed in the range of less than 1 km, 3–4 km and 8–12 km, among which the 3–4 km and 8–12 km intervals are on both sides of the Yellow River; In terms of distance from the river, the distribution of earthquake-induced landslide points is exponential (R 2 = 0.7727), mainly distributed on both sides of the river and nearby; For the distance factor from the earthquake center, the first level in the study is counted as 5 km, and the second and subsequent levels are counted as 10 km. The analysis found that the landslide points are distributed exponentially (R 2 = 0.9068). At the same time, the distribution statistics of landslide points were also conducted based on Land use, Soil texture, NDVI and Population distribution factors (Fig. 5 ).In terms of Land use, earthquake-induced landslide points mainly occur in cropland, some grasslands, and some landslide also occur near water bodies. In terms of soil texture, earthquake-induced landslide points are mainly distributed in loam, with a small amount distributed in clay(light) and loam sand layers. In terms of NDVI, earthquake-induced landslide risks are mainly distributed between 0.08 and 0.16; Judging from the Population distribution in the earthquake-affected areas, human activities are more intense in places with high population density, and therefore the distribution of earthquake-induced landslide points is also correspondingly more. 4.2 Response analysis of impact factors on landslides 1) Model evaluation accuracy The landslide points data and the selected 14 impact factors were input into the MaxEnt model. After 10 iterative calculations, the AUC value was finally obtained to be 0.854 (Fig. 6 ), and the model reliability reached a “good” level. Therefore, this study used the interpreted landslide points and various impact factors, and constructed the MaxEnt model through 10 iterative calculations to evaluate the risk of landslides induced by the Jishishan Ms6.2 earthquake. The results have good reliability. 2) Analysis of the importance of impact factors Importance is an indicator that reflects the degree of model dependence on the variable 42 . Table 5 shows the contribution rate and replacement importance of each impact factors to the impact degree of landslide disasters. It can be seen that the top five impact factors are distance from fracture zone, elevation, population distribution, soil texture data and distance from river. Their contribution rates were 39.0%, 38.1%, 17.8%, 1.3% and 1.2% respectively, and their cumulative contribution rate accounted for as high as 97.4%. As can be seen, The top five impact factors of replacement importance are distance from fracture zone, elevation, distance from river, population distribution and soil texture data, The replacement importance is 48.3%, 45.1%, 2.4%, 1.4% and 1.3% respectively, with a cumulative value of 98.5%. Table 5 Contribution rate and replacement importance of the main disaster-causing factors Serial No Factors Contribution rate/% replacement importance/% 1 Distance from fracture zone 39 48.3 2 Elevation 38.1 45.1 3 Population distribution 17.8 1.4 4 Soil texture data 1.3 1.3 5 Distance from river 1.2 2.4 6 NDVI 0.8 0.6 7 Slope 0.8 0.1 8 Distance from road 0.6 0.5 9 Aspect 0.3 0.2 10 TWI 0.1 0 11 Land use 0 0 12 Sectional curvature 0 0 13 Flat curvature 0 0 14 Curvature 0 0 Figure 7 shows the test results of the importance of each impact factors through the jackknife test method. From the test gain value 43 (Fig. 7 a), it can be seen that the top five impact factors are distance from fracture zone, elevation, population distribution, soil texture data, and NDVI, with values of 0.35、0.32、0.22、0.12 and 0.1, respectively. According to the AUC values (Fig. 7 b), the top five impact factors are elevation, distance to the fault zone, population distribution, NDVI, and distance to the river, with values of 0.74, 0.73, 0.67, 0.65, and 0.59, respectively. From the regularized training gain (Fig. 7 c), it can be seen that the top five impact factors are elevation, distance to the fault zone, population distribution, distance to the road, and distance to the river, with values of 0.3, 0.29, 0.22, 0.1, and 0.08, respectively. 3) Analysis of the response of influencing factors to landslide risk Figures 8 and 9 are the response curves of various impact factors to landslide occurrence, where the vertical axis represents the probability of landslide occurrence and the horizontal axis represents the value range of each factors. The reference probability threshold is set to 0.5. When it is greater than 0.5, it is considered that the value range of this factors is conducive to the occurrence of disasters 44 . As shown in Fig. 8 , the aspect has the highest response to landslide occurrence. A certain range of values of other factors is also sensitive to landslide occurrence. For example, when TWI is greater than 4m, the probability is greater than 0.5, which is very likely to cause landslides. Similarly, when the elevation zone is between 1700 and 2250 m, the profile curvature is -4.2 to 3, the plane curvature is -3.9 to 4.1, and the combined curvature is -6 to 11, this range responds strongly to landslides. When the distance to the fault zone is less than 1.7 km, the distance to the river is less than 3.8 km, the distance to the road is less than 2 km, the Slope is less than 30°, and the population distribution is less than 20 people/km 2 , the probability is greater than 0.5, which is very likely to cause landslides. It can also be seen that when NDVI is less than − 0.04 and 0.06 to 0.15, the probability is greater than 0.5, and the response to landslide disasters in this section is better; in terms of land use factors (Fig. 9 ), the probabilities of cultivated land, grassland and water areas are all greater than 0.5, and landslide disasters are very likely to occur; for soil texture, the probability of sandy loam and loam is greater than 0.5, and landslide disasters are very likely to occur. 4.3 Landslide risk assessment This paper adopts the importance and correlation coefficient method of impact factors, calculates variance expansion factors test results method (Table 6 ), eliminates factors with strong collinearity (Planar curvature and Profile curvature), low contribution rate (Land use and Curvature) and correlation (Elevation), and then constructs a model with the remaining factors, calculates the maximum entropy results, and divides them into five levels according to the natural breakpoint method. Figure 10 shows the risk assessment results of the landslide induced by the Jishishan Ms6.2 earthquake obtained in this study. According to statistics, the area of extremely high risk zone is 49.38 km 2 , accounting for 0.84% of the total area of the study area; the area of high risk zone is 157.79 km 2 , accounting for 2.69% of the total area of the study area; the area of medium risk zone is 430.03 km 2 , accounting for 7.33% of the total area of the study area; the area of low risk zone is 526.07 km 2 , accounting for 8.96% of the total area of the study area; the area of extremely low risk zone is 4699.02 km 2 , accounting for 80.18% of the total area of the study area. It can be seen that since the earthquake occurred in winter, most places were seasonally frozen, so the landslides induced by this earthquake were mostly small, and the extremely high and high-risk landslides were relatively rare, mainly located in some areas on both sides of the Yellow River, which is consistent with the results of literature 33 . Table 6 Variance expansion factors test results Impact factor VIF Slope 1.4 TWI 1.26 Distance from road 1.22 Distance from river 1.13 NDVI 1.12 Distance from fracture zone 1.09 Aspect 1.03 Soil texture data 1.03 To further analyze the relationship between the risk zone and the earthquake intensity, the risk assessment results were superimposed on the earthquake intensity map 45 , and the results were statistically obtained (Table 7 ). The density of extremely high and high risk zones is mainly located in the earthquake intensity VIII zone, with an area of 21.2 km 2 , accounting for 26.38% of the VIII zone. The density of medium risk areas is mainly distributed in VII and VIII zone, with an area of 341.22 km 2 , accounting for 16.92% and 28.82% of the area of VII and VIII zone respectively. The low and very low risk zone are mainly distributed in VII and VI zones, accounting for 75.33% and 97.55% of the area respectively. This area is far away from the earthquake-causing zone, and the risk of earthquake-induced geological disasters is also relatively low. Table 7 Area percentage of different risk grades in different seismic intensity zones earthquake intensity extremely high risk/% high risk/% medium risk/% low risk/% very low risk/% VIII zone 6.91 19.47 28.82 11.76 33.05 VII zone 1.80 5.95 16.92 16.12 59.21 VI zone 0.03 0.23 2.19 6.15 91.40 5. Discussion 5.1 Analysis of landslide drivers The Ms6.2 earthquake in Jishishan not only caused serious casualties, but also directly led to a large number of mountain instability, collapse and landslide disasters. The multiple aftershocks after the earthquake further increased the risk of instability, collapse and landslides. This study combined high-resolution satellite images before and after the earthquake and used visual interpretation to obtain most of the landslide and collapse risk points in the earthquake zone to conduct a risk assessment of the earthquake zone. Among them, selecting effective features related to the geological disasters induced by this earthquake is the basis for constructing the MaxEnt model, which requires that these features should be able to reflect the intrinsic properties of the geological environment and external impacts factors, such as topography, geological structure, rainfall, seismic activity, etc. This study selected 14 impact factors from the perspectives of environmental variables such as topographical factors, fault zones, soil, roads, populations, land cover and plant cover. It mainly adopted the superposition statistical analysis of landslide points and impact factors, and achieved this by exploring their contribution rate and replacement importance. In terms of topographic factors, the relative height difference in the Jishishan earthquake zone is about 3,000m, and the topographic is undulating. The high-altitude areas have steep topographic slopes, and the stability of soil and rocks is relatively poor, especially in the 1,800-2300m range. Human activities are also relatively intense, and they are extremely susceptible to driving factors such as earthquakes, resulting in landslides. The landslide points interpreted this time are mostly distributed in the southeast slope in terms of slope factor. The reason is that it is winter in the earthquake zone and the ground on the shady slope is frozen. Therefore, the seasonal shallow permafrost does not induce many geological disasters. However, when it melts next year, driven by rainfall, the risk of landslides will increase further. Among the impact factors selected in this study, the distance to the road, the distance to the fault zone and the distance to the river are all driving factors. Field investigations after the earthquake found that the collapse and landslides induced by this earthquake mostly occurred in areas close to the road. There are many human activities in this area, especially road construction, which often destroys the original terrain and soil structure, further increasing the risk of landslides and collapses. The development of landslides is closely related to the location of the fault zone. Due to the concentrated crustal stress near the fault zone and the fragile geological structure, it is easily affected by external factors such as earthquakes, thus causing landslide disasters. At the same time, the erosion of the river often destroys the stability of the slope, leading to the occurrence of landslide disasters. This study found that among land use factors, cropland, which has the most frequent human activities, has changed its original geological structure, reduced soil stability and increased the risk of geological disasters due to a series of activities carried out by humans in the cropland area, such as farming, excavation, and filling. At the same time, it is also seen that finer and more sticky soils are softened by water, reducing the stability of the soil, while coarser and sandier soils are relatively less prone to geological disasters. In this landslide study, although NDVI is not a direct indicator of geological disasters, areas with large NDVI values have dense vegetation coverage. The physical effects of the roots and above-ground parts of these vegetation can increase soil stability and reduce soil erosion. Areas with less vegetation coverage also have lower soil stability, thereby increasing the risk of geological disasters. 5.2 Selection of impact factors The selection of impact factors is a key link in the reliability of earthquake-induced landslide risk assessment 46 . Different from previous studies, this study selected impact factors based on environmental variables such as topographical factors, fault zones, soil, roads, population, land use and Plant cover. On the basis of analyzing the Response curve of impact factors to landslide risk, the main impact factors were selected by obtaining contribution rate and replacement importance and using the jackknife method to evaluate the importance of impact factors. The study found that the contribution rate and replacement importance of distance from the fault zone, elevation, population distribution, soil texture and distance from the river are all greater than 1, among which the distance from the fault zone is the largest, at 39.0% and 48.3%, respectively. To further analyze the reliability of this study, the jackknife method was used to test the importance of each impact factors. The three indicators of test gain value, AUC value and regularized training gain were calculated, and it was found that the main response factors to the risk of landslide induced by this earthquake were the distance from the fault zone, elevation and population distribution. It can be seen that in addition to the elevation factor, which is a disaster-prone environmental condition, the distance from the fault zone and population distribution are the main driving factors for the landslide risk induced by this earthquake. Since the relative height difference in the earthquake zone is about 3000m and the topographical is undulating, the stability of soil and rock in high-altitude areas is relatively poor due to the steep slope of the topographical, especially in the 1700-2300m range. Human activities (such as road construction, house building, farming, etc.) are also relatively intense. Remote sensing interpretation found that most of them occurred in areas close to roads. Road construction often destroys the original terrain and soil structure, creating conditions for the development of landslides and collapses. Due to the concentration of crustal stress and fragile geological structure, highly susceptible to landslide disasters driven by external factors such as earthquakes; At the same time, the erosion of rivers often destroys the stability of slopes, leading to landslide disasters. Field investigations have found that although the geological disasters induced by earthquakes are not severe and are mainly small-scale collapses and landslides, the distribution area, scale and density of disasters are consistent with the main impact factors calculated and determined in this study. For example, Fig. 11 shows typical landslides obtained during field geological disaster surveys, among which Figs. 11 a and 11 b are sand/mudstone landslides, which are mostly distributed in the inner slope areas of roads, etc. Figures 11 c and 11 d are loess landslides, which mostly occur in the slope cutting areas of houses and roads, etc. Figures 11 e is a ground fissure, which is developed in the terraced residential areas on both sides of the Yellow River in the earthquake zone. 6. Conclusion This paper obtains landslide points by visually interpreting Gaofen-1 and Jilin-1 images before and after the Jishishan Ms6.2 earthquake. A feature set is established by collecting 14 influencing factors. The MaxEnt model is trained using the feature set. The parameters and weights of the model are determined by the optimization algorithm to construct the MaxEnt model. The landslide risk assessment after the earthquake is also carried out. The research conclusions are as follows: From the perspective of topographic factors, the landslide points induced by this earthquake are mainly distributed in the 1700-2250m elevation zone and the slope range of 20–25°, and most of them occur in the sunny slopes facing east, southeast and south. They basically show an exponential distribution on TWI, and are widely distributed within a distance of 1.5 km from the road and 5 km from the earthquake center. From the perspective of land use, the earthquake-induced landslide points mainly occur in cropland, and the soil texture is mostly loam areas; in terms of plant cover, they are mainly concentrated in the NDVI range of 0.2 ~ 0.4 and the places with high population density in the earthquake zone. Based on the contribution rate and replacement importance of the impact factors, the test gain value calculated by the jackknife method, the AUC value and the regularized training gain value, the main impact factors of the risk of geological disasters induced by this earthquake are: distance from the fault zone, elevation and population distribution. When the distance from the fault zone is less than 1.7 km and the population distribution is 20 people/km 2 , the probability is greater than 0.5, and the response to the occurrence of landslide risk is more obvious. Based on the constructed MaxEnt model, it is concluded that the high risk zone for landslides in the earthquake zone are mainly distributed on both sides of the Yellow River and in the surrounding areas. Among them, the density of extremely high and high risk areas is mainly located in the earthquake intensity VIII zone, with an area of 21.2 km 2 , accounting for 26.38% of the area of VIII zone; the density of medium-risk areas is mainly distributed in VII and VIII zones, with area percentages of 16.92% and 28.82% respectively; the low and very low risk zone are mainly distributed in VII and VI zones, with area percentages of 75.33% and 97.55% respectively. Declarations Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Fundings This paper and its accompanying research were funded by the National Natural Science Foundation of China (42261069). We would like to express our heartfelt gratitude to the anonymous reviewers for their constructive and valuable comments and suggestions. Author Contribution Hao Wang: Methodology, Data procession, Writing – original draft. Quanfu Niu: Funding acquisition, Methodology, Writing – review & editing. Xi´an Cheng and Gang Wang: Data processing and analysis. Validation, Writing – review & editing. Data Availability The data that has been used is confidential. If any scholars would like to receive data from this study, please contact Hao Wang and email [email protected] . References Wan, Y.; Guo, J.; Ma F.S.; Liu, J.; Song, Y.W. Landslide susceptibility assessment based on MaxEnt model of along Sino-Nepal traffic corridor. The Chinese Journal of Geological Hazard and Control. 2022, 33(2), 88-95. Dai, L.X.; Xu, Q.; Fan, X.M.; Chang, M.; Yang, Q.; Yang, F.; Ren, J. 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Landslide hazard and risk assessment using semi-automatically created landslide inventories. Geomorphology. 2013, 184, 139-150. Hoo, Z.H.; Candlish, J.; Teare, D. What is an ROC curve?. Emergency Medicine Journal. 2017, 34(6): 357-359. Qasimi, A. B.; Isazade, V.; Berndtsson, R. Flood susceptibility prediction using MaxEnt and frequency ratio modeling for Kokcha River in Afghanistan. Natural Hazards. 2024, 120(2), 1367-1394. Li, H.Y.; Wang, Q.; Li, M.; Zang, X.Y.; Wang, Y.X. Identification of urban waterlogging indicators and risk assessment based on MaxEnt Model: A case study of Tianjin Downtown. Ecological Indicators. 2024, 158: 111354. Yang, Z.J.; Pang, B.; Dong, W.F.; Li, D.H. Spatial pattern and intensity mapping of coseismic landslides triggered by the 2022 Luding earthquake in China. Remote Sensing. 2023, 15(5), 1323. Wang, L.M.; Xu, S.Y.; Wang, P.; Wang, R.; Che, A.; Zhou, Y.G.; Wu, Z.J.; Wang, Q.; Pu, X.W.; Chai, S.F.; Ma, X.Y. Characteristics and lessons of liquefaction-triggered large-scale flow slide in loess deposit during Jishishan M6. 2 earthquake in 2023. Chinese Journal of Geotechnical Engineering. 2024, 46(2): 235-243. Niu, Q.F.; Dang, X.H.; Li, Y.F.; Zhang, Y.X.; Lu, X.L.; Gao, W.X. Suitability analysis for topographic factors in loess landslide research: a case study of Gangu County, China. Environmental earth sciences. 2018, 77, 1-12. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4598625","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":333011604,"identity":"dac8c438-d98d-4bbf-8cfa-10b6dbbd43ce","order_by":0,"name":"WANG HAO","email":"","orcid":"","institution":"Lanzhou University of Technology","correspondingAuthor":false,"prefix":"","firstName":"WANG","middleName":"","lastName":"HAO","suffix":""},{"id":333011605,"identity":"3d2a6b7f-e3b1-4866-82de-6dad3ee71ea7","order_by":1,"name":"Niu 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15:54:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1605698,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of landslides\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4598625/v1/eed0d965eab4ddf0bde0def3.png"},{"id":62132242,"identity":"3a3ba1dc-ca20-4704-9670-70a16186984c","added_by":"auto","created_at":"2024-08-09 15:46:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93514,"visible":true,"origin":"","legend":"\u003cp\u003eDistributions of disasters on terrain factors\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4598625/v1/fba24c825b393c9496fdd751.png"},{"id":62132238,"identity":"cad41571-fe47-4057-9548-89126f506bd8","added_by":"auto","created_at":"2024-08-09 15:46:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":89016,"visible":true,"origin":"","legend":"\u003cp\u003eDistributions of disasters on distance factors\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4598625/v1/f4be288bdf42abb9f9f8c5df.png"},{"id":62132237,"identity":"46913464-d8c3-4327-a5cb-294568b06471","added_by":"auto","created_at":"2024-08-09 15:46:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":74225,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of disasters on LULC, soil texture, population density and NDVI\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4598625/v1/19c11cec7a04efa2e58809ed.png"},{"id":62133029,"identity":"abc149cc-215e-4637-a0db-f7b47f7a8486","added_by":"auto","created_at":"2024-08-09 15:54:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":176124,"visible":true,"origin":"","legend":"\u003cp\u003eResults of ROC evaluation\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4598625/v1/4fe1302291e5eafba3fa3b58.png"},{"id":62133964,"identity":"e2bd1b08-b09e-4e3d-89aa-62533e2f2c12","added_by":"auto","created_at":"2024-08-09 16:02:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":253903,"visible":true,"origin":"","legend":"\u003cp\u003eResults of jackknife test for disaster-causing factors(Note:DFFZ express Distance from fracture zone;DFRI express Distance from river;DFRO express Distance from road;SC express Sectional curvature;PD express Population distribution;FC express Flat curvature;STD express Soil texture data)\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4598625/v1/78a563889fdb3c85ba25d92a.png"},{"id":62132247,"identity":"9496b289-9bcd-46c8-8018-10f1e48bde17","added_by":"auto","created_at":"2024-08-09 15:46:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":140575,"visible":true,"origin":"","legend":"\u003cp\u003eResponse curve of disaster-causing factors\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4598625/v1/2acf8fa5c045d28027a15a1c.png"},{"id":62133030,"identity":"cda0bf2f-6b1a-45b0-9ea3-f39a063e8578","added_by":"auto","created_at":"2024-08-09 15:54:19","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":35071,"visible":true,"origin":"","legend":"\u003cp\u003eHistogram of the response of Land use and soil texture data\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4598625/v1/758f01ec3caf63ee1a5d9b99.png"},{"id":62132246,"identity":"51b04919-3b63-45cd-88b1-5b05adff550b","added_by":"auto","created_at":"2024-08-09 15:46:19","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1204767,"visible":true,"origin":"","legend":"\u003cp\u003eResult of landslide risk evaluation for Ms6.2 earthquake-induced landslides in Jieshishan\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-4598625/v1/75b836617e0e86495a97cc58.png"},{"id":62132248,"identity":"35202fe4-0f40-4a79-8b2c-11d5a1c3b025","added_by":"auto","created_at":"2024-08-09 15:46:20","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1527347,"visible":true,"origin":"","legend":"\u003cp\u003eTypical landslides in earthquake area(a~b-sand/mudstone landslide; a~b-loess landslide; e-ground fissure)\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-4598625/v1/205463919244143e70d51cbd.png"},{"id":76628109,"identity":"56355d6f-80ca-42cb-b702-e0257bc3ac58","added_by":"auto","created_at":"2025-02-19 06:05:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9010491,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4598625/v1/1fbf418d-1fef-4ee4-906e-988622ebae34.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk assessment of landslides induced by the Ms6.2 earthquake in Jishishan of Gansu province, China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAt 23:59 on December 18, 2023, a magnitude 6.2 earthquake occurred in Liugou Township, Jishishan County, Linxia Prefecture, Gansu Province, with a focal depth of 10km(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cenc.ac.cn\u003c/span\u003e\u003cspan address=\"https://www.cenc.ac.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). As of 8:00 a.m. on the 22nd, the earthquake caused 117 deaths and 781 injuries in Gansu, and 34 deaths and 198 injuries in Qinghai. The earthquake was a thrust type earthquake with a maximum intensity of 8 degrees \u003csup\u003e1\u003c/sup\u003e. After the earthquake, a large number of mountains became unstable, collapsed and landslides occurred. The multiple aftershocks after the earthquake increased the risk of instability, collapse and landslides, which had a far-reaching influence on local residents.\u003c/p\u003e \u003cp\u003eEarthquakes often trigger a large number of geological disasters such as collapse, landslides, and mud-rock flows, which seriously threaten the lives and property safety of local people \u003csup\u003e2\u003c/sup\u003e. Because China is located in the southeastern part of the Eurasian Plate, it is affected by the compression, collision and subduction of the Indian Plate and the Pacific Plate. The crust is deformed strongly and it is the most earthquake-active region in the world \u003csup\u003e3\u003c/sup\u003e. Especially on the eastern edge of the Qinghai-Tibet Plateau, the geological structure is complex, the topography is diverse, and there are dense fault zones. Geological disasters occur frequently, causing numerous casualties and huge property losses in the area \u003csup\u003e4,5\u003c/sup\u003e. For example, the Ms7.5 Diexi earthquake in Maoxian, Sichuan in 1933 caused a large number of landslides and collapses on both sides of the Minjiang River \u003csup\u003e6\u003c/sup\u003e. The Wenchuan earthquake on May 12, 2008 triggered no fewer than 48,000 landslides \u003csup\u003e7\u003c/sup\u003e, affecting an area of approximately 711.8 km\u003csup\u003e2\u003c/sup\u003e and causing more than 60,000 deaths \u003csup\u003e8\u003c/sup\u003e; On April 14, 2010, the Yushu Ms7.1 earthquake triggered 2,036 landslides \u003csup\u003e9\u003c/sup\u003e, affecting an area of 1.194 km\u003csup\u003e2\u003c/sup\u003e and causing no less than 3,000 deaths \u003csup\u003e10\u003c/sup\u003e. on August 13, 2014, the Ludian Ms6.5 earthquake in Yunnan triggered 3,883 landslides \u003csup\u003e11\u003c/sup\u003e, affecting an area of 16.42 km\u003csup\u003e2\u003c/sup\u003e; on August 8, 2017, the Jiuzhaigou Ms7.0 earthquake in Sichuan caused 25 deaths \u003csup\u003e12\u003c/sup\u003e; and in 2022, the Luding Mw6.7 earthquake triggered 297 landslides, affecting an area of 1.5 km\u003csup\u003e2 13\u003c/sup\u003e. Compared with the losses caused by the Minxian Ms6.6 earthquake in 2013, the Jiuzhaigou Ms7.0 earthquake in 2017, and the Luding Ms6.9 earthquake in 2022, the losses caused by the Jishishan earthquake in Gansu on December 18, 2023 were greater \u003csup\u003e14\u003c/sup\u003e. According to data, geological disasters have occurred frequently in this area in history \u003csup\u003e15,16\u003c/sup\u003e. Therefore, conducting a geological risk assessment after the Jishishan earthquake is of practical significance for ensuring the safety of people\u0026rsquo;s property and lives.\u003c/p\u003e \u003cp\u003eSince the 21st century, a large number of scholars have do research on the assessment of geological disaster risk. From the perspective of research methods, most of them are based on historical mountain torrent disasters and adopt empirical evaluation methods \u003csup\u003e17\u0026ndash;20\u003c/sup\u003e, statistical analysis methods\u003csup\u003e21\u003c/sup\u003e or mathematical model methods \u003csup\u003e22\u0026ndash;24\u003c/sup\u003e. Among them, the empirical method is easily affected by the subjective factors of the evaluator; the statistical analysis method requires a large number of flash flood disaster samples and predictive evaluation by analyzing the distribution patterns of the samples; although the mathematical model method is widely used because of its reliable evaluation results, its difficulty lies in the determination of the weights of disaster-causing factors. With the development of computers and machine learning algorithms, non-linear algorithms such as machine learning are introduced into the study of flood disasters, and the regularity of the training samples can be used to avoid the determination of the weight of the disaster factors. For example, Tan uses neural networks and GIS to conduct geological hazard risk assessment on Wanli District, Nanchang \u003csup\u003e25\u003c/sup\u003e. Ma uses six deep learning algorithms to evaluate the risk of geological disasters \u003csup\u003e26\u003c/sup\u003e. Jena uses convolutional neural networks to evaluate the risk of geological disasters in northern India \u003csup\u003e27\u003c/sup\u003e. XIE uses Bayesian optimization\u0026ndash;support vector machine to conduct risk assessment of Nanping City \u003csup\u003e28\u003c/sup\u003e. Zhao uses machine learning algorithms to evaluate risk of risk of Hengduan Mountain \u003csup\u003e29\u003c/sup\u003e. Chen used random forest and radial basis function to conduct risk assessment of flood disasters in the Yangtze River Delta \u003csup\u003e30\u003c/sup\u003e. At present, geological risk assessment has become a hot topic in the field of disaster prevention and mitigation. Although the above algorithms have achieved good results, they did not consider the response, contribution rate and importance of influencing factor on risk assessment results, and Maxent solved this problem. Most studies are mainly focused on the characteristic analysis and risk assessment of impact factors and disasters such as landslides. There are fewer studies on the response analysis of impact factors to landslides, especially the contribution rate and importance analysis of influencing factors to the risk of disasters.\u003c/p\u003e \u003cp\u003eTo this end, this paper takes the Jishishan Ms6.2 earthquake area in Gansu as the test area. Based on multi-source remote sensing data, after in-depth discussion of the characteristic distribution between landslide risk and impact factors, as well as the response analysis of impact factors to landslides, a rapid assessment and analysis of the geological disaster risk in the earthquake area is carried out, in order to provide a reference for post-disaster rescue and reconstruction work.\u003c/p\u003e"},{"header":"2. Materials and Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1.Study area\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Ms6.2 earthquake in Jieshishan, Gansu, was located at the junction between Gansu and Qinghai provinces, with a center at 35.7\u0026deg;N-102.79\u0026deg;E and a depth of 10 km (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The earthquake affected 3 cities (states) and 9 counties (cities, districts) in Gansu Province, and 2 cities (states) and 4 counties (cities) in Qinghai Province.The earthquake area is a transition zone from the first step of the Tibetan Plateau to the second step of the Loess Plateau, with a large difference in elevation, the maximum difference being more than 3,000 meters, and strong activity of fault zones in the area, such as the south and north fault zones of the Laji Mountains, the Inverted Flowing River-Linxia Fault Zone, and the northern edge of the West Qinling Ridge, which is the area where earthquakes and other natural disasters are frequently occurring \u003csup\u003e31,32\u003c/sup\u003e. The earthquake caused a large number of casualties and property losses in the quake area, and also directly led to a large number of mountain destabilization, collapse and landslide disasters, and many aftershocks after the earthquake increased the risk of destabilization collapse and landslide.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2.Data\u003c/h2\u003e \u003c/div\u003e\n\u003ch3\u003e1) Satellite image data\u003c/h3\u003e\n\u003cp\u003eThe optical remote sensing images used this time are Gaofen-1 and Jilin-1, with spatial resolutions of 2m and 0.75m respectively. The pre-earthquake images were taken on December 18, and the post-earthquake images were taken on December 20 and December 19 respectively. Image preprocessing mainly includes processing such as radiometric calibration, atmospheric correction, geometric correction and image fusion, and finally obtains true-color high-resolution images, which are mainly used for the visual interpretation of landslide points \u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e2) Seismic point data\u003c/h3\u003e\n\u003cp\u003eThe geographic locations of the earthquakes and aftershocks used were obtained from the China National Earthquake Data Center(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://search.asf.alaska.edu/#/\u003c/span\u003e\u003cspan address=\"https://search.asf.alaska.edu/#/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ). This data is for magnitudes greater than 3.0 or greater as of December 20, 2023 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSeismic site data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime of earthquake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDepth (km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMagnitude (Ms)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNote\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023-12-18T23:59:30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026deg;42\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u0026deg;47\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJieshishan county\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eepicentre\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023-12-19T00:24:49.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026deg;44\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u0026deg;47\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJieshishan county\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eaftershock\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023-12-19T00:36:18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026deg;47\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u0026deg;47\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJieshishan county\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eaftershock\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023-12-19T00:43:12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026deg;47\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u0026deg;46\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJieshishan county\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eaftershock\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023-12-19T00:56:51.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026deg;42\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u0026deg;47\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJieshishan county\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eaftershock\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023-12-19T00:59:11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026deg;44\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u0026deg;46\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJieshishan county\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eaftershock\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023-12-19T00:59:39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026deg;50\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u0026deg;47\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJieshishan county\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eaftershock\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023-12-19T01:10:31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026deg;48\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u0026deg;47\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJieshishan county\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eaftershock\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023-12-19T01:20:12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026deg;48\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u0026deg;46\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJieshishan county\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eaftershock\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023-12-19T02:10:06.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026deg;50\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u0026deg;46\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJieshishan county\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eaftershock\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023-12-19T00:32:52.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026deg;46\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u0026deg;47\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJieshishan county\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eaftershock\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e3) Impact factors data\u003c/h3\u003e\n\u003cp\u003eThe impact factors data used in this paper mainly include topographical, fault zone, soil, road, populations, land cover and plant cover. 14 impact factors were obtained through data processing \u003csup\u003e20,21\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among them, 1) the topographic data is the ALOS digital elevation model (DEM) with a spatial resolution of 12.5 m, which is mainly used to obtain slope, aspect, sectional curvature, flat curvature, curvature, distance from rivers and topographic wetness index (TWI); 3) Road data, from the OSG ( Open Street Map) official website, used to calculate the distance from road; 4) Land use (LULC), which is the CLCD (China Land Cover Dataset) dataset of Wuhan University, with a spatial resolution of 30m, mainly including: corrpland, forest, shrub, grassland, water, snow/ice, barren and impervious; 5) Normalized Difference Vegetation Index (NDVI), from China National Tibetan Plateau Data Center, with a data source of MODIS and a spatial resolution of 250 m; 6) Population distribution, from WorldPop global population data, with a spatial resolution of 100 meters, downloaded from the GEE (Google Earth Engine) platform; 7) Soil texture data, from the World Soil Database, with a spatial resolution of 1 km.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImpact factors data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpact factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData sources\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eTopographical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation(m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eDEM data downloaded from ASF website(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://search.asf.alaska.edu/#/\u003c/span\u003e\u003cspan address=\"https://search.asf.alaska.edu/#/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), The other factors are DEM derived data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope(\u0026ordm;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAspect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSectional curvature\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlat curvature\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurvature\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from rivers(km)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTopographic wetness index(TWI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFault zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from fault zones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina National Earthquake Data Center(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://search.asf.alaska.edu/#/\u003c/span\u003e\u003cspan address=\"https://search.asf.alaska.edu/#/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil texture data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina National Cryosphere Deser Data Center( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncdc.ac.cn/\u003c/span\u003e\u003cspan address=\"http://www.ncdc.ac.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e )\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from roads(km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOSM Official Website( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.openstreetmap.org/\u003c/span\u003e\u003cspan address=\"https://www.openstreetmap.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e )\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation distribution(people/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWuhan University CLCD dataset( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zenodo.org/\u003c/span\u003e\u003cspan address=\"https://zenodo.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e )\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormalized Difference Vegetation Index(NDVI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina National Tibetan Plateau Science Data Center( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.tpda.ac.cn/home\u003c/span\u003e\u003cspan address=\"https://data.tpda.ac.cn/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e )\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"3. Method","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Maximum Entropy model\u003c/h2\u003e \u003cp\u003eMaximum Entropy(MaxEnt) model is a method to predict the probability distribution of random events based on the principle of maximum entropy, which has been widely used in the fields of disaster risk evaluation and predicting species distribution \u003csup\u003e34\u003c/sup\u003e. The principle of geohazard prediction using the MaxEnt model is to establish a probabilistic model of geohazard risk based on the relationship between geohazard occurrences and impact factors, so as to predict the geohazard risk of the whole region. This paper takes 14 impact factors as constraints and landslide points as events to seek the maximum entropy of geological disaster risk under 14 constraints, and then risk assessment of landslide.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data preprocessing\u003c/h2\u003e \u003cp\u003eThe preprocessing of the 1205 landslide points data from visual interpretation includes 1) elimination of duplicated and erroneous points obtained from visual interpretation of landslides; 2) removal of auto-correlation points by establishing a fishing net; 3) data conversion, which mainly involves converting the final 980 landslide points data into csv format for model construction.\u003c/p\u003e \u003cp\u003ePreprocessing of the impact factors, including resampling the factors to a uniform image scale, cropping so that all impact factors have the same row and column numbers, and then converting them to ASCII format was used to construct the MaxEnt model \u003csup\u003e35\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Model Parameter Setting\u003c/h2\u003e \u003cp\u003eModel parameter tuning is an important part of building a MaxEnt model. The specific process includes, the output format is set to logistic, and the landslide points are divided into test set and training set, in which the proportion of random test set is set to 25% and 75% is used for model training \u003csup\u003e36\u003c/sup\u003e. Accuracy was assessed using ROC (Create response curves, ROC), with the Regularization Multiplier set to 1 and the number of repetitive modeling sessions set to 10, in order to prevent the occurrence of underfitting and overfitting phenomena and to optimize the model construction \u003csup\u003e37\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Multilinear analysis\u003c/h2\u003e \u003cp\u003eVariance Inflation Factor (VIF) is a statistical measure of the severity of multicollinearity \u003csup\u003e38\u003c/sup\u003e. It indicates the extent to which the variance of a given independent variable is affected by multicollinearity. The calculation formula is shown below:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"351\" height=\"54\"\u003e\u003c/p\u003e \u003cp\u003ewhere VIF is the value of the variance inflation factor of the impact factor X\u003csub\u003ei\u003c/sub\u003e, R\u003csup\u003e2\u003c/sup\u003e is the proportion of variance of the impact factor X\u003csub\u003ei\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eVIF takes values from 1 to infinity. The closer the VIF value is to 1, the less the variance of the impact factor is affected by the other impact factors. A larger value of VIF indicates a stronger linear correlation between the independent variables, As shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVIF evaluation criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIF Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCovariance evaluation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIF\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo covariance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026thinsp;\u0026lt;\u0026thinsp;VIF\u0026thinsp;\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlight covariance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026thinsp;\u0026lt;\u0026thinsp;VIF\u0026thinsp;\u0026le;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium covariance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026thinsp;\u0026lt;\u0026thinsp;VIF\u0026thinsp;\u0026le;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelatively strong covariance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u0026thinsp;\u0026lt;\u0026thinsp;VIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrong covariance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Risk evaluation\u003c/h2\u003e \u003cp\u003eFor the classification of the grade of landslide risk evaluation results, with reference to the previous research results \u003csup\u003e39,40\u003c/sup\u003e and combined with the spatial distribution of landslide points obtained by visual interpretation of high resolution images, the grade of risky area is divided into five grades: extremely high risk zone, high risk zone, medium risk zone, low risk zone and very low risk zone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Accuracy evaluation\u003c/h2\u003e \u003cp\u003eThe ROC curve and AUC value (Area Under the ROC Curve are used in model accuracy evaluation as a common tool to assess the performance of binary classification models. The ROC curve describes the relationship between the sensitivity and specificity of the model at different thresholds, and the closer the ROC curve is to the upper left corner, the better the performance of the model \u003csup\u003e41\u003c/sup\u003e. The specific evaluation criteria for AUC values are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAUC evaluation criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecision Evaluation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0, 0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery poor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.6, 0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.7, 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.8, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.9, 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Result","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Distribution characteristics of factors affecting landslide points\u003c/h2\u003e \u003cp\u003eThis paper uses a visual interpretation of landslides by comparing Gaofen-1 and Jilin-1 satellite images before and after the Jieshishan Ms6.2 earthquake. Because there was a large amount of snowfall in the area before the earthquake, new soil was exposed in the area where the landslide occurred after the earthquake, which was conducive to the visual interpretation of satellite images of the landslide points. The principles of this visual interpretation are: Images with high spatial resolution in the preferred area are selected. If there are cloud cover or terrain shadows, images with similar time phases will be selected in order of spatial resolution from high to low, ultimately covering the entire earthquake zone. In the visual interpretation of landslide points, the main method used was to compare pre-earthquake and post-earthquake images. A total of 1,205 landslides and potential disaster points were catalogued in this interpretation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Most of them were small collapses and landslides, mainly concentrated in the loess hilly areas on both sides of the Yellow River in the earthquake zone, near roads and valleys, and mostly developed on steep slopes of houses and roads \u003csup\u003e33\u003c/sup\u003e. The main risk-bearing objects threatened were roads and farmland.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn order to further analyze the distribution characteristics of landslide points on various impact factors, the landslide points were superimposed on various factors in this study, and histograms were made for statistical analysis, where the horizontal axis was the classification of each factors and the vertical axis was the density.\u003c/p\u003e \u003cp\u003eFor each Topographical factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e),In the analysis, the elevation factor was divided into nine levels at intervals of 100 m. The superimposed statistical analysis showed that the earthquake-induced landslide points were basically parabolic in elevation factor distribution (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.7394), mainly distributed in the 1700\u0026ndash;2250 m elevation zone; Regarding the distribution of landslide points on the slope, the slope was classified into 5\u0026deg; intervals. Statistics show that landslide points mainly occurred in the range of less than 7\u0026deg; and 20\u0026ndash;25\u0026deg;, and were scattered in the range of slopes greater than 30\u0026deg;; On TWI, the occurrence of landslide points basically presents an exponential distribution (R2\u0026thinsp;=\u0026thinsp;0.6816); in terms of slope distribution, most of the earthquake-induced landslide disaster points occur in the east, southeast and south.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor various distance factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the distance factor from the road was classified into buffer zones with equal intervals of 0.5 km. The analysis found that the vast majority of landslide points induced by earthquakes occurred within 1.5 km of the road; For the distance from the fault zone, the first level is 1 km, and the second level and above are 2 km. The analysis shows that the earthquake-induced landslide points basically present an exponential distribution in the distance from the fault zone (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.201), mainly distributed in the range of less than 1 km, 3\u0026ndash;4 km and 8\u0026ndash;12 km, among which the 3\u0026ndash;4 km and 8\u0026ndash;12 km intervals are on both sides of the Yellow River; In terms of distance from the river, the distribution of earthquake-induced landslide points is exponential (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.7727), mainly distributed on both sides of the river and nearby; For the distance factor from the earthquake center, the first level in the study is counted as 5 km, and the second and subsequent levels are counted as 10 km. The analysis found that the landslide points are distributed exponentially (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9068).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the same time, the distribution statistics of landslide points were also conducted based on Land use, Soil texture, NDVI and Population distribution factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).In terms of Land use, earthquake-induced landslide points mainly occur in cropland, some grasslands, and some landslide also occur near water bodies. In terms of soil texture, earthquake-induced landslide points are mainly distributed in loam, with a small amount distributed in clay(light) and loam sand layers. In terms of NDVI, earthquake-induced landslide risks are mainly distributed between 0.08 and 0.16; Judging from the Population distribution in the earthquake-affected areas, human activities are more intense in places with high population density, and therefore the distribution of earthquake-induced landslide points is also correspondingly more.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Response analysis of impact factors on landslides\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1) Model evaluation accuracy\u003c/h3\u003e\n\u003cp\u003eThe landslide points data and the selected 14 impact factors were input into the MaxEnt model. After 10 iterative calculations, the AUC value was finally obtained to be 0.854 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), and the model reliability reached a \u0026ldquo;good\u0026rdquo; level. Therefore, this study used the interpreted landslide points and various impact factors, and constructed the MaxEnt model through 10 iterative calculations to evaluate the risk of landslides induced by the Jishishan Ms6.2 earthquake. The results have good reliability.\u003c/p\u003e\n\u003ch3\u003e2) Analysis of the importance of impact factors\u003c/h3\u003e\n\u003cp\u003eImportance is an indicator that reflects the degree of model dependence on the variable \u003csup\u003e42\u003c/sup\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the contribution rate and replacement importance of each impact factors to the impact degree of landslide disasters. It can be seen that the top five impact factors are distance from fracture zone, elevation, population distribution, soil texture data and distance from river. Their contribution rates were 39.0%, 38.1%, 17.8%, 1.3% and 1.2% respectively, and their cumulative contribution rate accounted for as high as 97.4%. As can be seen, The top five impact factors of replacement importance are distance from fracture zone, elevation, distance from river, population distribution and soil texture data, The replacement importance is 48.3%, 45.1%, 2.4%, 1.4% and 1.3% respectively, with a cumulative value of 98.5%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContribution rate and replacement importance of the main disaster-causing factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerial No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContribution rate/%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereplacement importance/%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from fracture zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation distribution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil texture data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from river\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAspect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSectional curvature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlat curvature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurvature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the test results of the importance of each impact factors through the jackknife test method. From the test gain value \u003csup\u003e43\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea), it can be seen that the top five impact factors are distance from fracture zone, elevation, population distribution, soil texture data, and NDVI, with values of 0.35、0.32、0.22、0.12 and 0.1, respectively. According to the AUC values (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb), the top five impact factors are elevation, distance to the fault zone, population distribution, NDVI, and distance to the river, with values of 0.74, 0.73, 0.67, 0.65, and 0.59, respectively. From the regularized training gain (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec), it can be seen that the top five impact factors are elevation, distance to the fault zone, population distribution, distance to the road, and distance to the river, with values of 0.3, 0.29, 0.22, 0.1, and 0.08, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e3) Analysis of the response of influencing factors to landslide risk\u003c/h3\u003e\n\u003cp\u003e \u003c/p\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e are the response curves of various impact factors to landslide occurrence, where the vertical axis represents the probability of landslide occurrence and the horizontal axis represents the value range of each factors. The reference probability threshold is set to 0.5. When it is greater than 0.5, it is considered that the value range of this factors is conducive to the occurrence of disasters \u003csup\u003e44\u003c/sup\u003e. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the aspect has the highest response to landslide occurrence. A certain range of values of other factors is also sensitive to landslide occurrence. For example, when TWI is greater than 4m, the probability is greater than 0.5, which is very likely to cause landslides. Similarly, when the elevation zone is between 1700 and 2250 m, the profile curvature is -4.2 to 3, the plane curvature is -3.9 to 4.1, and the combined curvature is -6 to 11, this range responds strongly to landslides. When the distance to the fault zone is less than 1.7 km, the distance to the river is less than 3.8 km, the distance to the road is less than 2 km, the Slope is less than 30\u0026deg;, and the population distribution is less than 20 people/km\u003csup\u003e2\u003c/sup\u003e, the probability is greater than 0.5, which is very likely to cause landslides. It can also be seen that when NDVI is less than \u0026minus;\u0026thinsp;0.04 and 0.06 to 0.15, the probability is greater than 0.5, and the response to landslide disasters in this section is better; in terms of land use factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), the probabilities of cultivated land, grassland and water areas are all greater than 0.5, and landslide disasters are very likely to occur; for soil texture, the probability of sandy loam and loam is greater than 0.5, and landslide disasters are very likely to occur.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Landslide risk assessment\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis paper adopts the importance and correlation coefficient method of impact factors, calculates variance expansion factors test results method (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), eliminates factors with strong collinearity (Planar curvature and Profile curvature), low contribution rate (Land use and Curvature) and correlation (Elevation), and then constructs a model with the remaining factors, calculates the maximum entropy results, and divides them into five levels according to the natural breakpoint method. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows the risk assessment results of the landslide induced by the Jishishan Ms6.2 earthquake obtained in this study. According to statistics, the area of extremely high risk zone is 49.38 km\u003csup\u003e2\u003c/sup\u003e, accounting for 0.84% of the total area of the study area; the area of high risk zone is 157.79 km\u003csup\u003e2\u003c/sup\u003e, accounting for 2.69% of the total area of the study area; the area of medium risk zone is 430.03 km\u003csup\u003e2\u003c/sup\u003e, accounting for 7.33% of the total area of the study area; the area of low risk zone is 526.07 km\u003csup\u003e2\u003c/sup\u003e, accounting for 8.96% of the total area of the study area; the area of extremely low risk zone is 4699.02 km\u003csup\u003e2\u003c/sup\u003e, accounting for 80.18% of the total area of the study area. It can be seen that since the earthquake occurred in winter, most places were seasonally frozen, so the landslides induced by this earthquake were mostly small, and the extremely high and high-risk landslides were relatively rare, mainly located in some areas on both sides of the Yellow River, which is consistent with the results of literature \u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariance expansion factors test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpact factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from river\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from fracture zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil texture data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo further analyze the relationship between the risk zone and the earthquake intensity, the risk assessment results were superimposed on the earthquake intensity map \u003csup\u003e45\u003c/sup\u003e, and the results were statistically obtained (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The density of extremely high and high risk zones is mainly located in the earthquake intensity VIII zone, with an area of 21.2 km\u003csup\u003e2\u003c/sup\u003e, accounting for 26.38% of the VIII zone. The density of medium risk areas is mainly distributed in VII and VIII zone, with an area of 341.22 km\u003csup\u003e2\u003c/sup\u003e, accounting for 16.92% and 28.82% of the area of VII and VIII zone respectively. The low and very low risk zone are mainly distributed in VII and VI zones, accounting for 75.33% and 97.55% of the area respectively. This area is far away from the earthquake-causing zone, and the risk of earthquake-induced geological disasters is also relatively low.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea percentage of different risk grades in different seismic intensity zones\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eearthquake intensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eextremely high risk/%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehigh risk/%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emedium risk/%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003elow risk/%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003every low risk/%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIII zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVII zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e59.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVI zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e91.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Analysis of landslide drivers\u003c/h2\u003e \u003cp\u003eThe Ms6.2 earthquake in Jishishan not only caused serious casualties, but also directly led to a large number of mountain instability, collapse and landslide disasters. The multiple aftershocks after the earthquake further increased the risk of instability, collapse and landslides. This study combined high-resolution satellite images before and after the earthquake and used visual interpretation to obtain most of the landslide and collapse risk points in the earthquake zone to conduct a risk assessment of the earthquake zone. Among them, selecting effective features related to the geological disasters induced by this earthquake is the basis for constructing the MaxEnt model, which requires that these features should be able to reflect the intrinsic properties of the geological environment and external impacts factors, such as topography, geological structure, rainfall, seismic activity, etc. This study selected 14 impact factors from the perspectives of environmental variables such as topographical factors, fault zones, soil, roads, populations, land cover and plant cover. It mainly adopted the superposition statistical analysis of landslide points and impact factors, and achieved this by exploring their contribution rate and replacement importance. In terms of topographic factors, the relative height difference in the Jishishan earthquake zone is about 3,000m, and the topographic is undulating. The high-altitude areas have steep topographic slopes, and the stability of soil and rocks is relatively poor, especially in the 1,800-2300m range. Human activities are also relatively intense, and they are extremely susceptible to driving factors such as earthquakes, resulting in landslides. The landslide points interpreted this time are mostly distributed in the southeast slope in terms of slope factor. The reason is that it is winter in the earthquake zone and the ground on the shady slope is frozen. Therefore, the seasonal shallow permafrost does not induce many geological disasters. However, when it melts next year, driven by rainfall, the risk of landslides will increase further. Among the impact factors selected in this study, the distance to the road, the distance to the fault zone and the distance to the river are all driving factors. Field investigations after the earthquake found that the collapse and landslides induced by this earthquake mostly occurred in areas close to the road. There are many human activities in this area, especially road construction, which often destroys the original terrain and soil structure, further increasing the risk of landslides and collapses. The development of landslides is closely related to the location of the fault zone. Due to the concentrated crustal stress near the fault zone and the fragile geological structure, it is easily affected by external factors such as earthquakes, thus causing landslide disasters. At the same time, the erosion of the river often destroys the stability of the slope, leading to the occurrence of landslide disasters. This study found that among land use factors, cropland, which has the most frequent human activities, has changed its original geological structure, reduced soil stability and increased the risk of geological disasters due to a series of activities carried out by humans in the cropland area, such as farming, excavation, and filling. At the same time, it is also seen that finer and more sticky soils are softened by water, reducing the stability of the soil, while coarser and sandier soils are relatively less prone to geological disasters. In this landslide study, although NDVI is not a direct indicator of geological disasters, areas with large NDVI values have dense vegetation coverage. The physical effects of the roots and above-ground parts of these vegetation can increase soil stability and reduce soil erosion. Areas with less vegetation coverage also have lower soil stability, thereby increasing the risk of geological disasters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Selection of impact factors\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe selection of impact factors is a key link in the reliability of earthquake-induced landslide risk assessment \u003csup\u003e46\u003c/sup\u003e. Different from previous studies, this study selected impact factors based on environmental variables such as topographical factors, fault zones, soil, roads, population, land use and Plant cover. On the basis of analyzing the Response curve of impact factors to landslide risk, the main impact factors were selected by obtaining contribution rate and replacement importance and using the jackknife method to evaluate the importance of impact factors. The study found that the contribution rate and replacement importance of distance from the fault zone, elevation, population distribution, soil texture and distance from the river are all greater than 1, among which the distance from the fault zone is the largest, at 39.0% and 48.3%, respectively. To further analyze the reliability of this study, the jackknife method was used to test the importance of each impact factors. The three indicators of test gain value, AUC value and regularized training gain were calculated, and it was found that the main response factors to the risk of landslide induced by this earthquake were the distance from the fault zone, elevation and population distribution. It can be seen that in addition to the elevation factor, which is a disaster-prone environmental condition, the distance from the fault zone and population distribution are the main driving factors for the landslide risk induced by this earthquake. Since the relative height difference in the earthquake zone is about 3000m and the topographical is undulating, the stability of soil and rock in high-altitude areas is relatively poor due to the steep slope of the topographical, especially in the 1700-2300m range. Human activities (such as road construction, house building, farming, etc.) are also relatively intense. Remote sensing interpretation found that most of them occurred in areas close to roads. Road construction often destroys the original terrain and soil structure, creating conditions for the development of landslides and collapses. Due to the concentration of crustal stress and fragile geological structure, highly susceptible to landslide disasters driven by external factors such as earthquakes; At the same time, the erosion of rivers often destroys the stability of slopes, leading to landslide disasters. Field investigations have found that although the geological disasters induced by earthquakes are not severe and are mainly small-scale collapses and landslides, the distribution area, scale and density of disasters are consistent with the main impact factors calculated and determined in this study. For example, Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows typical landslides obtained during field geological disaster surveys, among which Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eb are sand/mudstone landslides, which are mostly distributed in the inner slope areas of roads, etc. Figures\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ec and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ed are loess landslides, which mostly occur in the slope cutting areas of houses and roads, etc. Figures\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ee is a ground fissure, which is developed in the terraced residential areas on both sides of the Yellow River in the earthquake zone.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis paper obtains landslide points by visually interpreting Gaofen-1 and Jilin-1 images before and after the Jishishan Ms6.2 earthquake. A feature set is established by collecting 14 influencing factors. The MaxEnt model is trained using the feature set. The parameters and weights of the model are determined by the optimization algorithm to construct the MaxEnt model. The landslide risk assessment after the earthquake is also carried out. The research conclusions are as follows:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFrom the perspective of topographic factors, the landslide points induced by this earthquake are mainly distributed in the 1700-2250m elevation zone and the slope range of 20\u0026ndash;25\u0026deg;, and most of them occur in the sunny slopes facing east, southeast and south. They basically show an exponential distribution on TWI, and are widely distributed within a distance of 1.5 km from the road and 5 km from the earthquake center. From the perspective of land use, the earthquake-induced landslide points mainly occur in cropland, and the soil texture is mostly loam areas; in terms of plant cover, they are mainly concentrated in the NDVI range of 0.2\u0026thinsp;~\u0026thinsp;0.4 and the places with high population density in the earthquake zone.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBased on the contribution rate and replacement importance of the impact factors, the test gain value calculated by the jackknife method, the AUC value and the regularized training gain value, the main impact factors of the risk of geological disasters induced by this earthquake are: distance from the fault zone, elevation and population distribution. When the distance from the fault zone is less than 1.7 km and the population distribution is 20 people/km\u003csup\u003e2\u003c/sup\u003e, the probability is greater than 0.5, and the response to the occurrence of landslide risk is more obvious.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBased on the constructed MaxEnt model, it is concluded that the high risk zone for landslides in the earthquake zone are mainly distributed on both sides of the Yellow River and in the surrounding areas. Among them, the density of extremely high and high risk areas is mainly located in the earthquake intensity VIII zone, with an area of 21.2 km\u003csup\u003e2\u003c/sup\u003e, accounting for 26.38% of the area of VIII zone; the density of medium-risk areas is mainly distributed in VII and VIII zones, with area percentages of 16.92% and 28.82% respectively; the low and very low risk zone are mainly distributed in VII and VI zones, with area percentages of 75.33% and 97.55% respectively.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFundings\u003c/h2\u003e \u003cp\u003eThis paper and its accompanying research were funded by the National Natural Science Foundation of China (42261069). We would like to express our heartfelt gratitude to the anonymous reviewers for their constructive and valuable comments and suggestions.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHao Wang: Methodology, Data procession, Writing \u0026ndash; original draft. Quanfu Niu: Funding acquisition, Methodology, Writing \u0026ndash; review \u0026amp; editing. Xi\u0026acute;an Cheng and Gang Wang: Data processing and analysis. Validation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that has been used is confidential. If any scholars would like to receive data from this study, please contact Hao Wang and email [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWan, Y.; Guo, J.; Ma F.S.; Liu, J.; Song, Y.W. Landslide susceptibility assessment based on MaxEnt model of along Sino-Nepal traffic corridor. The Chinese Journal of Geological Hazard and Control. 2022, 33(2), 88-95.\u003c/li\u003e\n\u003cli\u003eDai, L.X.; Xu, Q.; Fan, X.M.; Chang, M.; Yang, Q.; Yang, F.; Ren, J. A preliminary study on spatial distribution patterns of landslides triggered by Jiuzhaigou earthquake in Sichuan on August 8th, 2017 and their susceptibility assessment. Journal of Engineering Geology. 2017, 25(4): 1151-1164.\u003c/li\u003e\n\u003cli\u003eDong, S.W.; Zhang, Y.D.; Chen, X.H.; Shi, J. Advances in structural geology and tectonics in the late 20th century: A review. Acta Geologica Sinica‐English Edition. 2016, 80(3), 349-375.\u003c/li\u003e\n\u003cli\u003eLi, Z.H.; Zhu, W.; Yu, C.; Zhang, Q.; Yang, Y.X.; Development status and trends of Imaging Geodesy. Acta Geodaetica et Cartographica Sinica. 2017, 2(11): 1805.\u003c/li\u003e\n\u003cli\u003eCai, M.F.; Peng, H.; Ma, X.M.; Jiang, J.J. Evolution of the in situ rock strain observed at Shandan monitoring station during the M8. 0 earthquake in Wenchuan, China. International Journal of Rock Mechanics and Mining Sciences. 2009, 46(5), 952-955.\u003c/li\u003e\n\u003cli\u003eChai, H.; Liu, H.; Zhang, Z. The Catalog of Chinese landslides dam events. Journal of Geological Hazards and Environment Preservation. 1995, (04):1-9.in Chinese\u003c/li\u003e\n\u003cli\u003eDai, F.C.; Xu, C.; Yao, X.; Xu, L.; Tu, X.B.; Gong Q.M. Spatial distribution of landslides triggered by the 2008 Ms 8.0 Wenchuan earthquake, China. Journal of Asian Earth Sciences. 2011, 40(4): 883-895.\u003c/li\u003e\n\u003cli\u003eTao, H.P.; Liu, B.T.; Liu, S.Z.; Fan, J.R.; Y, L. Natural Hazards Monitoring Using Romote Sensing\u0026mdash;\u0026mdash;A Case Study of 5.12 Wenchuan Earthquake. Mountain Research., 2008, (03): 276-279.in Chinese\u003c/li\u003e\n\u003cli\u003eXu, C.; Xu, X.W.; Dai, F.C.; Wang, Y.Y. 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Characteristics and lessons of liquefaction-triggered large-scale flow slide in loess deposit during Jishishan M6. 2 earthquake in 2023. Chinese Journal of Geotechnical Engineering. 2024, 46(2): 235-243.\u003c/li\u003e\n\u003cli\u003eNiu, Q.F.; Dang, X.H.; Li, Y.F.; Zhang, Y.X.; Lu, X.L.; Gao, W.X. Suitability analysis for topographic factors in loess landslide research: a case study of Gangu County, China. Environmental earth sciences. 2018, 77, 1-12.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Jishishan Ms6.2 earthquake, landslide, factors, importance analysis, MaxEnt model, risk assessment","lastPublishedDoi":"10.21203/rs.3.rs-4598625/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4598625/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e2023-12-18T23:59, an earthquake measuring Ms6.2 occurred in Jishishan County, China, causing serious casualties and directly leading to the occurrence of a large number of landslides. After the earthquake, multiple aftershocks increased the risk of collapse and landslides. Based on high-resolution satellite images before and after the earthquake, a Maximum Entropy model was constructed using visually interpreted landslide points and impact factors characteristics to evaluate the risk of landslide disasters after the earthquake. The conclusions of the study are as follows: 1) The main distribution of earthquake-induced landslide disasters is in the elevation zone of 1800-2300m, on sunny slopes with a slope gradient of 20\u0026ndash;25\u0026deg;, which are mostly developed in the area 1.5 km away from the roads, 1.7 km away from the fault zones, and 5 km away from the earthquake center. The majority of the landslide occurred in cropland and loam areas with higher population density in the earthquake region. 2) Based on the contribution rate and replacement importance of the impact factors, test gain value, AUC value, and regularized training gain value, the main impact factors for landslide risk induced by the earthquake were comprehensively determined as follows: Distance from the fault zone, Elevation, and Population density. 3) Based on the constructed Maximum Entropy model, it is found that there is a good consistency between the extremely high and high risk areas of landslide disasters in the earthquake zone and the seismic intensity. Among them, the extremely high and high risk areas are mainly distributed in the intensity zone VIII, with an area of 5.368km\u003csup\u003e2\u003c/sup\u003e, accounting for 77.82% of the total area of the extremely high and high-risk zones. The low and very low risk areas are mainly distributed in the intensity zones VI and VII, accounting for 92.80% of the total area of the study region. This paper constructs a Maximum Entropy model based on the analysis of the importance of impact factors to evaluate the risk of landslide disasters in the earthquake zone. The research results provide references for post-disaster reconstruction in the earthquake zone.\u003c/p\u003e","manuscriptTitle":"Risk assessment of landslides induced by the Ms6.2 earthquake in Jishishan of Gansu province, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 15:46:14","doi":"10.21203/rs.3.rs-4598625/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b861a79-91eb-4366-b2ac-e0860402b015","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35256592,"name":"Earth and environmental sciences/Natural hazards"},{"id":35256593,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-02-19T05:55:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-09 15:46:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4598625","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4598625","identity":"rs-4598625","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}