Back to Search Start Over

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

Authors :
Yao Li
Peng Cui
Chengming Ye
José Marcato Junior
Zhengtao Zhang
Jian Guo
Jonathan Li
Source :
Remote Sensing, Vol 13, Iss 17, p 3436 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

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.

Details

Language :
English
ISSN :
20724292
Volume :
13
Issue :
17
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.8b7a2fe0d4dd499293c0120e3c41f22e
Document Type :
article
Full Text :
https://doi.org/10.3390/rs13173436