Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region

Landslidesusceptibility mapping (LSM) is the primary link of geological disaster risk evaluation, which is significant for postearthquake emergency response and rebuilding after disasters. Existing LSM studies applying deep learning (DL) methods have shortcomings such as easy overfitting, slow conve...

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Autores principales:	Yibing Xiong, Yi Zhou, Futao Wang, Shixin Wang, Jingming Wang, Jianwan Ji, Zhenqing Wang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Ant colony optimization (ACO) algorithm deep belief network (DBN) Jiuzhaigou earthquake landslide susceptibility mapping (LSM) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/09023736712f4ec1a7e310954a9dd38f
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spelling	oai:doaj.org-article:09023736712f4ec1a7e310954a9dd38f2021-11-18T00:00:15ZLandslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region2151-153510.1109/JSTARS.2021.3122825https://doaj.org/article/09023736712f4ec1a7e310954a9dd38f2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9591360/https://doaj.org/toc/2151-1535Landslidesusceptibility mapping (LSM) is the primary link of geological disaster risk evaluation, which is significant for postearthquake emergency response and rebuilding after disasters. Existing LSM studies applying deep learning (DL) methods have shortcomings such as easy overfitting, slow convergence, and insufficient hyperparametric optimization. In response to these problems, this study proposes an ensemble model based on ant colony optimization strategy and deep belief network (ACO-DBN). In ACO-DBN, DL optimization strategies were added to DBN and their combined parameters were optimized with ACO. Next, Pearson's correlation coefficient and random forest importance ranking methods were utilized to optimize landslide causative factors. Then, the Jiuzhaigou earthquake region was selected as an example to explore the applicability of this model. Besides, we conducted the Wilcoxon signed rank test in order to verify that the differences were statistically significant. In a comprehensive comparative all indexes and landslide density, the model proposed in this article shows good rationality, scientificity, and interpretability. The newly occurred landslide site further demonstrates that heuristically optimized DL could make scientific and accurate evaluation of landslide susceptibility.Yibing XiongYi ZhouFutao WangShixin WangJingming WangJianwan JiZhenqing WangIEEEarticleAnt colony optimization (ACO) algorithmdeep belief network (DBN)Jiuzhaigou earthquakelandslide susceptibility mapping (LSM)Ocean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11042-11057 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Ant colony optimization (ACO) algorithm deep belief network (DBN) Jiuzhaigou earthquake landslide susceptibility mapping (LSM) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	Ant colony optimization (ACO) algorithm deep belief network (DBN) Jiuzhaigou earthquake landslide susceptibility mapping (LSM) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Yibing Xiong Yi Zhou Futao Wang Shixin Wang Jingming Wang Jianwan Ji Zhenqing Wang Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region
description	Landslidesusceptibility mapping (LSM) is the primary link of geological disaster risk evaluation, which is significant for postearthquake emergency response and rebuilding after disasters. Existing LSM studies applying deep learning (DL) methods have shortcomings such as easy overfitting, slow convergence, and insufficient hyperparametric optimization. In response to these problems, this study proposes an ensemble model based on ant colony optimization strategy and deep belief network (ACO-DBN). In ACO-DBN, DL optimization strategies were added to DBN and their combined parameters were optimized with ACO. Next, Pearson's correlation coefficient and random forest importance ranking methods were utilized to optimize landslide causative factors. Then, the Jiuzhaigou earthquake region was selected as an example to explore the applicability of this model. Besides, we conducted the Wilcoxon signed rank test in order to verify that the differences were statistically significant. In a comprehensive comparative all indexes and landslide density, the model proposed in this article shows good rationality, scientificity, and interpretability. The newly occurred landslide site further demonstrates that heuristically optimized DL could make scientific and accurate evaluation of landslide susceptibility.
format	article
author	Yibing Xiong Yi Zhou Futao Wang Shixin Wang Jingming Wang Jianwan Ji Zhenqing Wang
author_facet	Yibing Xiong Yi Zhou Futao Wang Shixin Wang Jingming Wang Jianwan Ji Zhenqing Wang
author_sort	Yibing Xiong
title	Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region
title_short	Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region
title_full	Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region
title_fullStr	Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region
title_full_unstemmed	Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region
title_sort	landslide susceptibility mapping using ant colony optimization strategy and deep belief network in jiuzhaigou region
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/09023736712f4ec1a7e310954a9dd38f
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Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region

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