Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area
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...
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MDPI AG
2021-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/17/3436 |
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| author | Yao Li Peng Cui Chengming Ye José Marcato Junior Zhengtao Zhang Jian Guo Jonathan Li |
| author_facet | Yao Li Peng Cui Chengming Ye José Marcato Junior Zhengtao Zhang Jian Guo Jonathan Li |
| author_sort | Yao Li |
| collection | DOAJ |
| description | 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 (<i>OA</i>) 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. |
| first_indexed | 2024-03-10T08:05:04Z |
| format | Article |
| id | doaj.art-8b7a2fe0d4dd499293c0120e3c41f22e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T08:05:04Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8b7a2fe0d4dd499293c0120e3c41f22e2023-11-22T11:08:53ZengMDPI AGRemote Sensing2072-42922021-08-011317343610.3390/rs13173436Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source AreaYao Li0Peng Cui1Chengming Ye2José Marcato Junior3Zhengtao Zhang4Jian Guo5Jonathan Li6Key Laboratory of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, ChinaKey Laboratory of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, ChinaKey Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu 610059, ChinaFaculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, BrazilAcademy of Disaster Reduction and Emergency Management, Ministry of Emergency Management & Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaDepartment of Geological Engineering, Chang’an University, Xi’an 710064, ChinaDepartment of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, CanadaAn 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 (<i>OA</i>) 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.https://www.mdpi.com/2072-4292/13/17/3436spatial predictionearthquake-induced landslidesource area featurestacked autoencoder |
| spellingShingle | Yao Li Peng Cui Chengming Ye José Marcato Junior Zhengtao Zhang Jian Guo Jonathan Li Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area Remote Sensing spatial prediction earthquake-induced landslide source area feature stacked autoencoder |
| title | Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area |
| title_full | Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area |
| title_fullStr | Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area |
| title_full_unstemmed | Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area |
| title_short | Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area |
| title_sort | accurate prediction of earthquake induced landslides based on deep learning considering landslide source area |
| topic | spatial prediction earthquake-induced landslide source area feature stacked autoencoder |
| url | https://www.mdpi.com/2072-4292/13/17/3436 |
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