An Investigation into the Susceptibility to Landslides Using Integrated Learning and Bayesian Optimization: A Case Study of Xichang City

Fucheng Xing, Ning Li (), Boju Zhao, Han Xiang and Yutao Chen
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Fucheng Xing: College of Emergency Management, Xihua University, Chengdu 610039, China
Ning Li: College of Emergency Management, Xihua University, Chengdu 610039, China
Boju Zhao: College of Emergency Management, Xihua University, Chengdu 610039, China
Han Xiang: College of Emergency Management, Xihua University, Chengdu 610039, China
Yutao Chen: College of Emergency Management, Xihua University, Chengdu 610039, China

Sustainability, 2024, vol. 16, issue 20, 1-20

Abstract: In the middle southern section of the Freshwater River–Small River Fault system, Xichang City, Daliang Prefecture, Sichuan Province, is situated in the junction between the Anning River Fault and the Zemu River Fault. There has been a risk of increased activity in the fault zone in recent years, and landslide susceptibility evaluation for the area can effectively reduce the risk of disaster occurrence. Using integrated learning and Bayesian hyperparameter optimization, 265 landslides in Xichang City were used as samples in this study. Thirteen influencing factors were chosen to assess landslide susceptibility, and the BO-XGBoost, BO-LightGBM, and BO-RF models were evaluated using precision, recall, F1, accuracy, and AUC curves. The findings indicated that after removing the terrain relief evaluation factor, the four most significant factors associated with landslide susceptibility were NDVI, distance from faults, slope, and distance from rivers. The study demonstrates that the AUC value of the BO-XGBoost model in the study area is 0.8677, demonstrating a better generalization ability and higher prediction accuracy than the BO-LightGBM and BO-RF models. After Bayesian optimization of hyperparameters, the model offers a significant improvement in prediction accuracy.

Keywords: integrated learning; Bayesian optimization; landslide susceptibility; XGBoost; LightGBM; random forests (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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