Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong
文献类型：期刊论文
| 作者 | Chen, Yangyang1; Ming, Dongping1; Ling, Xiao1; Lv, Xianwei2; Zhou, Chenghu3
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| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
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| 出版日期 | 2021 |
| 卷号 | 14页码:3625-3639 |
| 关键词 | Terrain factors Numerical models Feature extraction Training Statistical analysis Geology Reliability Convolutional neural networks (CNNs) landslide susceptibility mapping (LSM) Lantau Island remote sensing |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2021.3066378 |
| 通讯作者 | Ming, Dongping(mingdp@cugb.edu.cn) |
| 英文摘要 | Landslide susceptibility mapping (LSM) is an effective way to predict spatial probability of landslide occurrence. Existing convolutional neural network (CNN)-based methods apply self-built CNN with simple structure, which failed to reach CNN's full potential on high-level feature extraction, meanwhile ignored the use of numerical predisposing factors. For the purpose of exploring feature fusion based CNN models with greater reliability in LSM, this study proposes an ensemble model based on channel-expanded pre-trained CNN and traditional machine learning model (CPCNN-ML). In CPCNN-ML, pre-trained CNN with mature structure is modified to excavate high-level features of multichannel predisposing factor layers. LSM result is generated by traditional machine learning (ML) model based on hybrid feature of high-level features and numerical predisposing factors. Lantau Island, Hong Kong is selected as study area; temporal landslide inventory is used for model training and evaluation. Experimental results show that CPCNN-ML has ability to predict landslide occurrence with high reliability, especially the CPCNN-ML based on random forest. Contrast experiments with self-built CNN and traditional ML models further embody the superiority of CPCNN-ML. It is worth noting that coastal regions are newly identified landslide-prone regions compared with previous research. This finding is of great reference value for Hong Kong authorities to formulate appropriate disaster prevention and mitigation policies. |
| WOS关键词 | CONVOLUTIONAL NEURAL-NETWORKS ; CLASSIFICATION ; PREDICTION ; MODEL ; AREA ; INTEGRATION ; HAZARD |
| 资助项目 | China Geological Survey[DD20191006] ; National Natural Science Foundation of China[41671369] ; National Key Research and Development Program[2017YFB0503600-05] ; Fundamental Research Funds for the Central Universities |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000640757900003 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | China Geological Survey ; National Natural Science Foundation of China ; National Key Research and Development Program ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/161616]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Ming, Dongping |
| 作者单位 | 1.China Univ Geosci Beijing, Sch Informat Engn, Beijing 100083, Peoples R China 2.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Yangyang,Ming, Dongping,Ling, Xiao,et al. Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2021,14:3625-3639. |
| APA | Chen, Yangyang,Ming, Dongping,Ling, Xiao,Lv, Xianwei,&Zhou, Chenghu.(2021).Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,14,3625-3639. |
| MLA | Chen, Yangyang,et al."Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14(2021):3625-3639. |
入库方式： OAI收割
来源：地理科学与资源研究所
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