A cellular automaton integrating spatial case-based reasoning for predicting local landslide hazards
文献类型：期刊论文
| 作者 | Chen, Jianhua3; Xu, Kaihang2,3; Zhao, Zheng1; Gan, Xianxia3; Xie, Huawei3 |
| 刊名 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
 |
| 出版日期 | 2023-11-15 |
| 页码 | 28 |
| ISSN号 | 1365-8816 |
| 关键词 | Landslide hazards spatial prediction geomorphology earth surface processes case-based reasoning cellular automaton |
| DOI | 10.1080/13658816.2023.2273877 |
| 通讯作者 | Xu, Kaihang(2017020377@stu.cdut.edu.cn) |
| 英文摘要 | Predicting landslide hazards benefits geological disaster prevention and control. A novel cellular automaton (CA) integrating spatial case-based reasoning (SCBR), namely SCBR-CA, is proposed in this paper to predict landslide hazards at a local scale. The proposed model not only extracts spatial scene features for computations but also achieves dynamic prediction, which means that only one input is needed to obtain continuous predictions. Experiments were performed in Lushan, Sichuan, China. After using a convolutional neural network (CNN) to obtain the initial static landslide hazard zoning results, the landslide hazard zoning results for 2016-2025 were predicted with the SCBR-CA model. For comparison, a CA combined with a CNN (CNN-CA), was introduced. The area under the curve (AUC) of the receiver operating characteristic curve and Moran's I index were used to assess the performance of the model. The experimental results showed that SCBR-CA yields slightly better AUC and Moran's I index values than CNN-CA, and the dynamically predicted landslide hazard zoning results are equivalent or superior to those of static zoning, which indicates that the SCBR-CA model effectively predict local landslide hazards. |
| WOS关键词 | NEURAL-NETWORK ; MODEL ; SYSTEM ; SIMULATIONS |
| 资助项目 | The authors would like to thank Prof. Shawn Laffan, Prof. May Yuan, and the three anonymous reviewers for their valuable comments. The authors also thank the Lushan Natural Resources and Planning Bureau, Lushan Public Security Bureau, Lushan Transportation ; Lushan Natural Resources and Planning Bureau, Lushan Public Security Bureau, Lushan Transportation Bureau, Yaan Public Meteorological Service Center, Sichuan Institute of Geological Survey, China Earthquake Network Center, Geospatial Data Cloud, United Sta |
| WOS研究方向 | Computer Science ; Geography ; Physical Geography ; Information Science & Library Science |
| 语种 | 英语 |
| 出版者 | TAYLOR & FRANCIS LTD |
| WOS记录号 | WOS:001102764100001 |
| 资助机构 | The authors would like to thank Prof. Shawn Laffan, Prof. May Yuan, and the three anonymous reviewers for their valuable comments. The authors also thank the Lushan Natural Resources and Planning Bureau, Lushan Public Security Bureau, Lushan Transportation ; Lushan Natural Resources and Planning Bureau, Lushan Public Security Bureau, Lushan Transportation Bureau, Yaan Public Meteorological Service Center, Sichuan Institute of Geological Survey, China Earthquake Network Center, Geospatial Data Cloud, United Sta |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/199568]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Xu, Kaihang |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 2.Sichuan Engn Technol Res Ctr Geol Disaster Prevent, Chengdu, Peoples R China 3.Chengdu Univ Technol, Coll Geophys, Chengdu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Jianhua,Xu, Kaihang,Zhao, Zheng,et al. A cellular automaton integrating spatial case-based reasoning for predicting local landslide hazards[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2023:28. |
| APA | Chen, Jianhua,Xu, Kaihang,Zhao, Zheng,Gan, Xianxia,&Xie, Huawei.(2023).A cellular automaton integrating spatial case-based reasoning for predicting local landslide hazards.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,28. |
| MLA | Chen, Jianhua,et al."A cellular automaton integrating spatial case-based reasoning for predicting local landslide hazards".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2023):28. |
入库方式： OAI收割
来源：地理科学与资源研究所
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