Interpretable Landslide Susceptibility Evaluation Based on Model Optimization

Haijun Qiu, Yao Xu, Bingzhe Tang (), Lingling Su, Yijun Li, Dongdong Yang and Mohib Ullah
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Haijun Qiu: Shaanxi Key Laboratory of Earth Surface and Environment Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Yao Xu: Shaanxi Key Laboratory of Earth Surface and Environment Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Bingzhe Tang: Shaanxi Key Laboratory of Earth Surface and Environment Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Lingling Su: Shaanxi Key Laboratory of Earth Surface and Environment Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Yijun Li: Shaanxi Key Laboratory of Earth Surface and Environment Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Dongdong Yang: Shaanxi Key Laboratory of Earth Surface and Environment Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Mohib Ullah: Shaanxi Key Laboratory of Earth Surface and Environment Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China

Land, 2024, vol. 13, issue 5, 1-20

Abstract: Machine learning (ML) is increasingly utilized in Landslide Susceptibility Mapping (LSM), though challenges remain in interpreting the predictions of ML models. To reveal the response relationship between landslide susceptibility and evaluation factors, an interpretability model was constructed to analyze how the results of the ML model are realized. This study focuses on Zhenba County in Shaanxi Province, China, employing both Random Forest (RF) and Support Vector Machine (SVM) to develop LSM models optimized through Random Search (RS). To enhance interpretability, the study incorporates techniques such as Partial Dependence Plot (PDP), Local Interpretable Model-Agnostic Explanations (LIMEs), and Shapley Additive Explanations (SHAP). The RS-optimized RF model demonstrated superior performance, achieving an Area Under the Curve (AUC) of 0.965. The interpretability model identified the NDVI and distance from road as important factors influencing landslides occurrence. NDVI plays a positive role in the occurrence of landslides in this region, and the landslide-prone areas are within 500 m from the road. These analyses indicate the importance of improved hyperparameter selection in enhancing model accuracy and performance. The interpretability model provides valuable insights into LSM, facilitating a deeper understanding of landslide formation mechanisms and guiding the formulation of effective prevention and control strategies.

Keywords: landslide; Random Forest; Support Vector Machine; hyperparameter selection; interpretability (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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