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Article

Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan

by 1,2, 1,2,*, 1, 1,3 and 4
1
School of Civil Engineering, Central South University, Changsha 410075, China
2
MOE Key Laboratory of Engineering Structures of Heavy-Haul Railway, Central South University, Changsha 410075, China
3
State Key Laboratory of Geohazard Prevention and Geo-Environment Protection, Chengdu University of Technology, Chengdu 610000, China
4
Department of Civil Engineering, Kyushu University, Fukuoka 819-0395, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Renato Morbidelli
Water 2021, 13(22), 3312; https://doi.org/10.3390/w13223312
Received: 3 November 2021 / Revised: 18 November 2021 / Accepted: 19 November 2021 / Published: 22 November 2021
(This article belongs to the Special Issue Remote Sensing and GIS for Geological Hazards Assessment)
Landslide represents an increasing menace causing huge casualties and economic losses, and rainfall is a predominant factor inducing landslides. Landslide susceptibility assessment (LSA) is a commonly used and effective method to prevent landslide risk, however, the LSA does not analyze the impact of the rainfall on landslides which is significant and non-negligible. Therefore, the spatiotemporal LSA considering the inducing effect of rainfall is proposed to improve accuracy and applicability. In this study, the influencing factors are selected using the chi-square test, out-of-bag error and multicollinearity test. The spatial LSA are thus obtained using the random forest (RF) model, deep belief networks model and support vector machine, and compared using receiver operating characteristic curve and seed cell area index to determine the optimal assessment result. According to the heavy rainfall characteristics in the study area, the rainfall period is divided into four stages, and the effective rainfall model is employed to generate the rainfall impact (RI) maps of the four stages. The spatiotemporal LSAs are obtained by coupling the optimal spatial LSA and various RI maps and verified using the landslide warning map. The results demonstrate that the optimal spatiotemporal LSA is obtained using the spatial LSA of the RF model and temporal LSA of the rainfall data in the peak stage. It can predict the area where rainfall-induced landslides are likely to occur and prevent landslide risk. View Full-Text
Keywords: landslide susceptibility; RF model; DBN model; SVM model; effective rainfall model; spatiotemporal LSA landslide susceptibility; RF model; DBN model; SVM model; effective rainfall model; spatiotemporal LSA
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MDPI and ACS Style

Li, J.; Wang, W.; Li, Y.; Han, Z.; Chen, G. Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan. Water 2021, 13, 3312. https://doi.org/10.3390/w13223312

AMA Style

Li J, Wang W, Li Y, Han Z, Chen G. Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan. Water. 2021; 13(22):3312. https://doi.org/10.3390/w13223312

Chicago/Turabian Style

Li, Jiaying, Weidong Wang, Yange Li, Zheng Han, and Guangqi Chen. 2021. "Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan" Water 13, no. 22: 3312. https://doi.org/10.3390/w13223312

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