Hazard Assessment of Shallow Loess Landslides Under Different Rainfall Intensities Based on the SINMAP Model: A Case Study of Yuzhong County
Source abstract
Abstract
The Loess Plateau is one of the most landslide-prone regions in China, where rainfall-induced shallow loess landslides severely constrain regional economic and social development. Therefore, investigating the stability of shallow loess slopes under rainfall conditions is of great significance. Taking Yuzhong County in Gansu Province as an example, this study uses the SINMAP model (Version 2.0) to assess slope stability. The areas of unstable zones under different rainfall intensities were identified, and the spatial distribution of hazard sites was analyzed to evaluate the applicability of this deterministic physical model in the study area. Furthermore, a Personnel Risk Level (PRL) determined by combining population density with the Stability Index (SI, defined as the probability that the factor of safety exceeds 1: SI = Prob (FS > 1)) was proposed and applied to assess the potential impact of landslides on local residents. The novelty of this study lies in three aspects: (1) targeting Yuzhong County (a loess region with scarce comprehensive landslide risk assessments) to fill the regional research gap, (2) quantifying PRL through a modified hazard index (HI = population density × (1/SI)) to achieve spatialized risk mapping for vulnerable populations, and (3) systematically analyzing the dynamic response of slope stability to five gradient rainfall intensities (from light rain to severe rainstorm) and verifying model sensitivity to key parameters. The results show that as rainfall intensity increases, stable areas gradually decrease while unstable areas expand, with stable zones progressively transforming into unstable ones. Greater rainfall intensity also leads to an increase in the number of landslides within unstable zones. The proposed PRL helps delineate the severity of hazards in different townships, providing new references for mitigating casualties and property losses caused by landslides.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Landslides and related hazards · Tree Root and Stability Studies · Soil erosion and sediment transport
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
The Third Geological and Mineral Exploration Institute of Gansu Provincial Bureau of Geology and Mineral Resources, Lanzhou 730050, China
School of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China
Institute of Geotechnical Engineering, Xi’an University of Technology, Xi’an 710048, China
Funding
the National Natural Science Foundation of China · Chang’an University · the Special Fund for Basic Scientific Research of Central Universities
References (46)
Show source-provided references
1. He, L., Wu, X., He, Z., Xue, D., Luo, F., Bai, W., Kang, G., Chen, X., and Zhang, Y. (2023). Susceptibility Assessment of Landslides in the Loess Plateau Based on Machine Learning Models: A Case Study of Xining City. Sustainability, 15.10.3390/su152014761 ↗
2. Xie, C., Huang, Y., Li, L., Li, T., and Xu, C. (2023). Detailed Inventory and Spatial Distribution Analysis of Rainfall-Induced Landslides in Jiexi County, Guangdong Province, China in August 2018. Sustainability, 15.10.3390/su151813930 ↗
3. Wei, L., Zeng, Z., and Yan, J. (2024). Factors Affecting the Stability of Loess Landslides: A Review. Appl. Sci., 14.10.3390/app14072735 ↗
4. Xiao, Y., Wei, L., and Liu, X. (2025). Failure Mechanism and Movement Process Inversion of Rainfall-Induced Landslide in Yuexi Country. Sustainability, 17.10.3390/su17125639 ↗
5. Liu, Z., Huang, J., Li, Y., Liu, X., Qiang, F., and He, Y. (2025). A Bibliometric Analysis of Geological Hazards Monitoring Technologies. Sustainability, 17.10.3390/su17030962 ↗
6. Trend and Spatiotemporal Distribution of Fatal Landslides Triggered by Non-Seismic Effects in China10.1007/s10346-018-1007-z ↗
7. Assessment of Landslide Susceptibility and Risk Factors in China10.1007/s11069-021-04812-8 ↗
8. Gao, H., and Zhang, X. (2022). Landslide Susceptibility Assessment Considering Landslide Volume: A Case Study of Yangou Watershed on the Loess Plateau (China). Appl. Sci., 12.10.3390/app12094381 ↗
9. Petrucci, O. (2022). Landslide Fatality Occurrence: A Systematic Review of Research Published between January 2010 and March 2022. Sustainability, 14.10.3390/su14159346 ↗
10. Dandridge, C., Stanley, T.A., Kirschbaum, D.B., and Lakshmi, V. (2023). Spatial and Temporal Analysis of Global Landslide Reporting Using a Decade of the Global Landslide Catalog. Sustainability, 15.10.3390/su15043323 ↗
11. Construction of Landslide Warning by Combining Rainfall Threshold and Landslide Susceptibility in the Gully Region of the Loess Plateau: A Case of Lanzhou City, China10.1016/j.jhydrol.2024.132148 ↗
12. Mao, J., Su, Q., Zhu, Y., Xiao, Y., Yan, T., and Zhang, L. (2025). Multi-Sensor Remote Sensing for Early Identification of Loess Landslide Hazards: A Comprehensive Approach. Appl. Sci., 15.10.3390/app15126890 ↗
13. Zhang, M., and Tang, X. (2025). Quantification and Analysis of Factors Influencing Territorial Spatial Conflicts in the Gully Region of the Loess Plateau: A Case Study of Qingyang City, Gansu Province, China. Sustainability, 17.10.20944/preprints202502.1907.v1 ↗
14. Effects of Changes in Soil Properties Caused by Progressive Infiltration of Rainwater on Rainfall-Induced Landslides10.1016/j.catena.2023.107475 ↗
15. Spatio-Temporal Analysis and Simulation on Shallow Rainfall-Induced Landslides in China Using Landslide Susceptibility Dynamics and Rainfall I-D Thresholds10.1007/s11430-016-9008-4 ↗
16. Shen, S., Deng, L., Tang, D., Chen, J., Fang, R., Du, P., and Liang, X. (2025). Landslide Hazard Assessment Based on Ensemble Learning Model and Bayesian Probability Statistics: Inference from Shaanxi Province, China. Sustainability, 17.10.3390/su17051973 ↗
17. Field Research on Preferential Infiltration in Rainfall-Induced Loess Landslides10.1016/j.enggeo.2025.108184 ↗
18. An, K., Kim, S., Chae, T., and Park, D. (2018). Developing an Accessible Landslide Susceptibility Model Using Open-Source Resources. Sustainability, 10.10.3390/su10020293 ↗
19. Yang, F.-Y., Zhuo, L., Xiao, M.-L., Xie, H.-Q., Liu, H.-Z., and He, J.-D. (2023). A Statistical Risk Assessment Model of the Hazard Chain Induced by Landslides and Its Application to the Baige Landslide. Appl. Sci., 13.10.3390/app13063577 ↗
20. Wei, Z., Li, Y., Dong, J., Cao, S., Ma, W., Wang, X., Wang, H., Tang, R., Zhao, J., and Liu, X. (2024). The Identification and Influence Factor Analysis of Landslides Using SBAS-InSAR Technique: A Case Study of Hongya Village, China. Appl. Sci., 14.10.3390/app14188413 ↗
21. Hou, C., Liu, H., Wang, X., Hu, J., Tang, Y., and Yao, X. (2025). Landslide Susceptibility Analysis Based on Dataset Construction of Landslides in Yiyang Using GIS and Machine Learning. Appl. Sci., 15.10.3390/app15105597 ↗
22. Chen, W., Sun, Z., and Han, J. (2019). Landslide Susceptibility Modeling Using Integrated Ensemble Weights of Evidence with Logistic Regression and Random Forest Models. Appl. Sci., 9.10.3390/app9010171 ↗
23. Huangfu, W., Wu, W., Zhou, X., Lin, Z., Zhang, G., Chen, R., Song, Y., Lang, T., Qin, Y., and Ou, P. (2021). Landslide Geo-Hazard Risk Mapping Using Logistic Regression Modeling in Guixi, Jiangxi, China. Sustainability, 13.10.3390/su13094830 ↗
24. Li, X., and Li, S. (2021). Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine. Appl. Sci., 11.10.3390/app11041381 ↗
25. Wang, H., Xu, J., Tan, S., and Zhou, J. (2023). Landslide Susceptibility Evaluation Based on a Coupled Informative–Logistic Regression Model—Shuangbai County as an Example. Sustainability, 15.10.3390/su151612449 ↗
26. Oh, H.-J., and Lee, S. (2017). Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree. Appl. Sci., 7.10.3390/app7101000 ↗
27. Ma, S., Chen, J., Wu, S., and Li, Y. (2023). Landslide Susceptibility Prediction Using Machine Learning Methods: A Case Study of Landslides in the Yinghu Lake Basin in Shaanxi. Sustainability, 15.10.3390/su152215836 ↗
28. Gao, F., Gao, X., Yang, C., and Li, J. (2024). Research on the Evolution Network Model of the Landslide Disaster Chain: A Case Study of the Baige Landslide. Appl. Sci., 14.10.3390/app14020499 ↗
29. Tynchenko, Y., Kukartsev, V., Tynchenko, V., Kukartseva, O., Panfilova, T., Gladkov, A., Nguyen, V., and Malashin, I. (2024). Landslide Assessment Classification Using Deep Neural Networks Based on Climate and Geospatial Data. Sustainability, 16.10.3390/su16167063 ↗
30. Ji, Q., Liang, Y., Xie, F., Yu, Z., and Wang, Y. (2024). Automatic and Efficient Detection of Loess Landslides Based on Deep Learning. Sustainability, 16.10.3390/su16031238 ↗
31. Li, M., and Tian, H. (2025). Insights from Optimized Non-Landslide Sampling and SHAP Explainability for Landslide Susceptibility Prediction. Appl. Sci., 15.10.3390/app15031163 ↗
32. Infinite Slope Stability Model and Steady-State Hydrology-Based Shallow Landslide Susceptibility Evaluations: The Guneysu Catchment Area (Rize, Turkey)10.1016/j.catena.2021.105161 ↗
33. Landslide Hazard Assessment of Rainfall-Induced Landslide Based on the CF-SINMAP Model: A Case Study from Wuling Mountain in Hunan Province, China10.1007/s11069-020-04483-x ↗
34. Gao, J., Shi, X., Li, L., Zhou, Z., and Wang, J. (2022). Assessment of Landslide Susceptibility Using Different Machine Learning Methods in Longnan City, China. Sustainability, 14.10.3390/su142416716 ↗
35. A Physically Based Model for the Topographic Control on Shallow Landsliding10.1029/93WR02979 ↗
36. Pack, R., Tarboton, D., and Goodwin, C. (1998, January 21–25). The SINMAP Approach to Terrain Stability Mapping. Proceedings of the 8th Congress of the International Association of Engineering Geology, Vancouver, BC, Canada.
37. Baum, R.L., Savage, W.Z., and Godt, J.W. (2008). TRIGRS—A Fortran Program for Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis, Version 2.0, US Geological Survey. Open-File Report.10.3133/ofr20081159 ↗
38. A Comparative Analysis of Terrain Stability Models for Predicting Shallow Landslides in Colluvial Soils10.1016/j.geomorph.2006.03.039 ↗
39. Feng, L., Guo, M., Wang, W., Chen, Y., Shi, Q., Guo, W., Lou, Y., Kang, H., Chen, Z., and Zhu, Y. (2023). Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling. Sustainability, 15.10.3390/su15010006 ↗
40. Prediction of Spatiotemporal Stability and Rainfall Threshold of Shallow Landslides Using the TRIGRS and Scoops3D Models10.1016/j.catena.2020.104999 ↗
41. Landslide Damage from Extreme Rainstorm Geological Accumulation Layers within Plain River Basins10.2112/SI82-001.1 ↗
42. Analysis of the Causes of Loess Disasters under Human Engineering Activities
43. Slope Stability Modelling with SINMAP in a Settlement Area of the Swabian Alb10.1007/s10346-009-0167-2 ↗
44. Evaluation of Shallow Loess Landslide Stability in Typical Loess Hilly Region: A Case Study of Zhidan County in Yan’an Area of Shaanxi Province
45. A study of the stability evaluation method of rainfall-induced shallow loess landslides based on the Maxent-Sinmap slope model
46. Regional-Scale Landslide Susceptibility Modelling in the Cordillera Blanca, Peru—A Comparison of Different Approaches10.1007/s10346-018-1090-1 ↗