Effect of time-variant rainfall on landslide susceptibility: A case study in Quang Ngai Province, Vietnam

Viet Long Doan; Ba-Quang-Vinh Nguyen; Chi Cong Nguyen; Cuong Tien Nguyen

doi:10.15625/2615-9783/20065

Author affiliations

Authors

Viet Long Doan The University of Danang, University of Science and Technology, Danang, Vietnam
Ba-Quang-Vinh Nguyen 1-School of Civil Engineering and Management, International University, Quarter 6, Linh Trung Ward, Thu Duc City, Ho Chi Minh City, Vietnam; 2-Vietnam National University, Ho Chi Minh City, Vietnam
Chi Cong Nguyen The University of Danang, University of Science and Technology, Danang, Vietnam
Cuong Tien Nguyen 1-Faculty of Vehicle and Energy Engineering, Phenikaa University, Hanoi 12116, Vietnam, Phenikaa; 2-Research and Technology Institute (PRATI), No. 167 Hoang Ngan, Cau Giay, Hanoi 11313, Vietnam

DOI:

https://doi.org/10.15625/2615-9783/20065

Keywords:

Time-variant rainfall, landslide susceptibility, XGBoost, Boruta, ROC

Abstract

Rainfall is a triggering factor that causes landslides, especially in the regions where landslides often occur after consecutive days of heavy rainfall. Most previous studies only used a specific rainfall map for landslide susceptibility assessment. However, this approach was unreasonable because rainfall is a time-variant data. This study uses the time series data of 1-day, 3-day, 5-day, and 7-day maximum precipitation from 2016 to 2020 in the mountainous area of Quang Ngai province for landslide susceptibility assessment. These data and other influencing factors were used to develop landslide spatial prediction models using the Extreme Gradient Boosting method. The prediction model's performance was assessed using the statistical index and receiver operating characteristic curve methods. The testing results of 4 cases using consecutive days of maximum rainfall data demonstrated excellent performance. Of these, the model with a 3-day maximum rainfall with ACC = 0.813, kappa = 0.625, SST = 0.872, SPF = 0.754, and
AUC = 0.895 had the best performance. In addition, these results were compared to the previous approach that used average annual rainfall. The validation result indicates that the cases using a time series of maximum precipitation (with AUC of approximately 0.9) outperform the cases with average annual rainfall (AUC=0.838). Finally, the model using 3-day maximum rainfall is then used for landslide spatial prediction mapping. These maps provide spatial prediction and assess landslide susceptibility corresponding to rainfall frequencies.

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