Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area

doi:10.3390/rs13173436

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所 > 山地灾害与地表过程重点实验室

	Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area
	Li, Yao 7,8; Cui, Peng 7,8; Ye, Chengming 6; Marcato Junior, Jose 5; Zhang, Zhengtao 3,4; Guo, Jian 2; Li, Jonathan 1
刊名	REMOTE SENSING
	2021-09-01
卷号	13 期号:17 页码:19
关键词	spatial prediction earthquake-induced landslide source area feature stacked autoencoder
ISSN号	2072-4292
DOI	10.3390/rs13173436
英文摘要	An earthquake-induced landslide (EQIL) is a rapidly changing process occurring at the Earth's surface that is strongly controlled by the earthquake in question and predisposing conditions. Predicting locations prone to EQILs on a large scale is significant for managing rescue operations and disaster mitigation. We propose a deep learning framework while considering the source area feature of EQIL to model the complex relationship and enhance spatial prediction accuracy. Initially, we used high-resolution remote sensing images and a digital elevation model (DEM) to extract the source area of an EQIL. Then, 14 controlling factors were input to a stacked autoencoder (SAE) to search for robust features by sparse optimization, and the classifier took advantage of high-level abstract features to identify the EQIL spatially. Finally, the EQIL inventory collected from the Wenchuan earthquake was used to validate the proposed model. The results show that the proposed method significantly outperformed conventional methods, achieving an overall accuracy (OA) of 91.88%, while logistic regression (LR), support vector machine (SVM), and random forest (RF) achieved 80.75%, 82.22%, and 84.16%, respectively. Meanwhile, this study reveals that shallow machine learning models only take advantage of significant factors for EQIL prediction, but deep learning models can extract more effective information related to EQIL distribution from low-value density data, which is why its prediction accuracy is growing with increasing input factors. There is hope that new knowledge of EQILs can be represented by high-level abstract features extracted by hidden layers of the deep learning model, which are typically acquired by statistical methods.
资助项目	Key Research Program of Frontier Sciences, CAS[QYZDY-SSW-DQC006] ; Second Tibetan Plateau Scientific Expedition and Research Program (STEP)[2019QZKK0906]
WOS关键词	SUSCEPTIBILITY ASSESSMENT ; NEURAL-NETWORKS ; RANDOM FOREST ; CLASSIFICATION ; STABILITY ; PROVINCE ; SICHUAN ; MODELS ; HAZARD ; REGION
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	MDPI
WOS记录号	WOS:000694534300001
资助机构	Key Research Program of Frontier Sciences, CAS ; Second Tibetan Plateau Scientific Expedition and Research Program (STEP)
内容类型	期刊论文
源URL	[http://ir.imde.ac.cn/handle/131551/56226]
专题	成都山地灾害与环境研究所_山地灾害与地表过程重点实验室
通讯作者	Cui, Peng
作者单位	1.Univ Waterloo, Dept Geog & Environm Management, Waterloo, ON N2L 3G1, Canada 2.Changan Univ, Dept Geol Engn, Xian 710064, Peoples R China 3.Beijing Normal Univ, Fac Geog Sci, Minist Educ, Beijing 100875, Peoples R China 4.Beijing Normal Univ, Acad Disaster Reduct & Emergency Management, Minist Emergency Management, Beijing 100875, Peoples R China 5.Univ Fed Mato Grosso do Sul, Fac Engn Architecture & Urbanism & Geog, BR-79070900 Campo Grande, MS, Brazil 6.Chengdu Univ Technol, Key Lab Earth Explorat & Informat Technol, Minist Educ, Chengdu 610059, Peoples R China 7.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 8.Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Hazards & Earth Surface Proc, Chengdu 610041, Peoples R China
推荐引用方式 GB/T 7714	Li, Yao,Cui, Peng,Ye, Chengming,et al. Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area[J]. REMOTE SENSING,2021,13(17):19.
APA	Li, Yao.,Cui, Peng.,Ye, Chengming.,Marcato Junior, Jose.,Zhang, Zhengtao.,...&Li, Jonathan.(2021).Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area.REMOTE SENSING,13(17),19.
MLA	Li, Yao,et al."Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area".REMOTE SENSING 13.17(2021):19.