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

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 o...

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Autores principales:	Jiaying Li, Weidong Wang, Yange Li, Zheng Han, Guangqi Chen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	landslide susceptibility RF model DBN model SVM model effective rainfall model spatiotemporal LSA Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
Acceso en línea:	https://doaj.org/article/f901648800b54d5bad490f5738d4e77d
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id	oai:doaj.org-article:f901648800b54d5bad490f5738d4e77d
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spelling	oai:doaj.org-article:f901648800b54d5bad490f5738d4e77d2021-11-25T19:16:46ZSpatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan10.3390/w132233122073-4441https://doaj.org/article/f901648800b54d5bad490f5738d4e77d2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4441/13/22/3312https://doaj.org/toc/2073-4441Landslide 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.Jiaying LiWeidong WangYange LiZheng HanGuangqi ChenMDPI AGarticlelandslide susceptibilityRF modelDBN modelSVM modeleffective rainfall modelspatiotemporal LSAHydraulic engineeringTC1-978Water supply for domestic and industrial purposesTD201-500ENWater, Vol 13, Iss 3312, p 3312 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	landslide susceptibility RF model DBN model SVM model effective rainfall model spatiotemporal LSA Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
spellingShingle	landslide susceptibility RF model DBN model SVM model effective rainfall model spatiotemporal LSA Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Jiaying Li Weidong Wang Yange Li Zheng Han Guangqi Chen Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan
description	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.
format	article
author	Jiaying Li Weidong Wang Yange Li Zheng Han Guangqi Chen
author_facet	Jiaying Li Weidong Wang Yange Li Zheng Han Guangqi Chen
author_sort	Jiaying Li
title	Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan
title_short	Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan
title_full	Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan
title_fullStr	Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan
title_full_unstemmed	Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan
title_sort	spatiotemporal landslide susceptibility mapping incorporating the effects of heavy rainfall: a case study of the heavy rainfall in august 2021 in kitakyushu, fukuoka, japan
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f901648800b54d5bad490f5738d4e77d
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Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan

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