Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling

Lanqian Feng, Mingming Guo (), Wenlong Wang (), Yulan Chen, Qianhua Shi, Wenzhao Guo, Yibao Lou, Hongliang Kang, Zhouxin Chen and Yanan Zhu
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Lanqian Feng: State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Water and Soil Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
Mingming Guo: State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Water and Soil Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
Wenlong Wang: State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Water and Soil Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
Yulan Chen: State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Water and Soil Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
Qianhua Shi: School of Environment and Resources, Taiyuan University of Science and Technology, Taiyuan 030024, China
Wenzhao Guo: State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Water and Soil Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
Yibao Lou: Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China
Hongliang Kang: Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China
Zhouxin Chen: State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Water and Soil Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
Yanan Zhu: Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China

Sustainability, 2022, vol. 15, issue 1, 1-18

Abstract: Shallow landslides restrict local sustainable socioeconomic development and threaten human lives and property in loess tableland. Therefore, the appropriate creation of risk maps is critical for mitigating shallow landslide disasters. The first task to be done was to evaluate the vulnerability of shallow landslides based on a machine learning model (random forest (RF), a support vector machine (SVM) and logistic regression (Log)), and a physical model (SINMAP) in the loess tableland area. By comparing the differences, the best method for evaluating the vulnerability of shallow landslide was selected. The nonlinear response relationship between shallow landslides and environmental factors was quantified based on the frequency ratio. Multicollinearity analysis was used to identify 10 factors that were applied on ML to construct the spatial distribution model. The SINMAP model used a DEM and soil physical parameters to determine the stability coefficient of the study area. The results showed that (1) shallow landslides in Dongzhiyuan mainly occurred on shady slopes with an elevation of 1068–1249 m, a slope gradient of 36°–60° and a concave shape. The stream power and stream transport indexes increased with increasing rainfall erosion, making shallow landslides likely. The susceptibility of shallow landslides changed parabolically with the change in the NDVI and mainly occurred in grassland and shrubland. (2) The four methods performed similarly in predicting the sensitivity of shallow landslides. The high-incidence areas were on both sides of eroded gully slopes. The tableland and gully bottom areas were not prone to shallow landslides. (3) The highest area under the curve (AUC) values were generated from the RF training and validation datasets of 0.92 and 0.93, respectively, followed by SVM AUC values of 0.91 and 0.92, respectively; Log AUC values of 0.91 and 0.89, respectively, and the SINMAP model AUC values of 0.69 and 0.74, respectively. In conclusion, the RF model best predicted the susceptibility of shallow landslides in the study area. The results provide a scientific basis for disaster mitigation on the Loess Plateau.

Keywords: shallow landslide; random forests; support vector machines; logistic regression; SINAMP; susceptibility assessment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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