Improved Shallow Landslide Susceptibility Prediction Based on Statistics and Ensemble Learning

Zhu Liang, Wei Liu, Weiping Peng, Lingwei Chen and Changming Wang
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Zhu Liang: College of Construction Engineering, South China University of Technology, Guangzhou 510641, China
Wei Liu: Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China
Weiping Peng: Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China
Lingwei Chen: Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China
Changming Wang: College of Construction Engineering, Jilin University, Changchun 130012, China

Sustainability, 2022, vol. 14, issue 10, 1-21

Abstract: Rainfall-induced landslides bring great damage to human life in mountain areas. Landslide susceptibility assessment (LSA) as an essential step toward landslide prevention has attacked a considerate focus for years. However, defining a reliable or accurate susceptibility model remains a challenge although various methods have been applied. The main purpose of this paper is to explore a comprehensive model with high reliability, accuracy, and intelligibility in LSA by combing statistical methods and ensemble learning techniques. Miyun country in Beijing is selected as the study area. Firstly, the dataset containing 370 landslide locations inventories and 13 conditioning factors were collected and non-landslide samples were prepared by clustering analysis. Secondly, random forest (RF), gradient boosting decision tree (GBDT), and adaptive boosting decision tree (Ada-DT) were selected as base learners for the Stacking ensemble method, and these methods were evaluated using measures like area under the curve (AUC). Finally, the Gini index and frequent ratio (FR) were combined to analyze the major conditioning factors. The results indicated that the performance of the Stacking method was enhanced with an AUC value of 0.944 while the basic classifiers also performed well with 0.906, 0.910, and 0.917 for RF, GBDT, and Ada-DT, respectively. Regions with a distance to a stream less than 2000 m, a distance to a road less than 3000 m, and elevation less than 600 m were susceptible to the landslide hazard. The conclusion demonstrates that the performance of LSA desires enhancement and the reliability and intelligibility of a model can be improved by combining binary and multivariate statistical methods.

Keywords: landslide susceptibility; statistical methods; ensemble techniques; GIS (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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