Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance

Li Zhuo, Yupu Huang, Jing Zheng (), Jingjing Cao and Donghu Guo
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Li Zhuo: Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China
Yupu Huang: Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China
Jing Zheng: Guangdong Climate Center, Guangzhou 501641, China
Jingjing Cao: Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China
Donghu Guo: Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China

Sustainability, 2023, vol. 15, issue 11, 1-23

Abstract: Landslides pose a serious threat to human lives and property. Accurate landslide susceptibility mapping (LSM) is crucial for sustainable development. Machine learning has recently become an important means of LSM. However, the accuracy of machine learning models is limited by the heterogeneity of environmental factors and the imbalance of samples, especially for large-scale LSM. To address these problems, we created an improved random forest (RF)-based LSM model and applied it to Guangdong Province, China. First, the RF-based LSM model was constructed using rainfall-induced landslide samples and 13 environmental factors and by exploring the optimal positive-to-negative and training-to-test sample ratios. Second, the performance of the RF-based LSM model was evaluated and compared with three other machine learning models. The results indicate that: (1) the proposed RF-based model has the best performance with the highest area under curve (AUC) of 0.9145, based on optimal positive-to-negative and training-to-test sample ratios of 1:1 and 8:2, respectively; (2) the introduction of rainfall and global human modification (GHM) can increase the AUC from 0.8808 to 0.9145; and (3) rainfall and topography are two dominant factors in Guangdong landslides. These findings can facilitate landslide risk prevention and serve as a technical reference for large-scale accurate LSM.

Keywords: landslide susceptibility mapping (LSM); machine learning; random forest (RF); sample balance; rainfall-induced landslide; global human modification (GHM) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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