A novel deep learning framework for landslide susceptibility assessment using improved deep belief networks with the intelligent optimization algorithm
文献类型：期刊论文
| 作者 | Meng, Shaoqiang1,3,4; Shi, Zhenming1,3,4; Li, Gang3; Peng, Ming1,4; Liu, Liu2; Zheng, Hongchao1,4; Zhou, Changshi3 |
| 刊名 | COMPUTERS AND GEOTECHNICS
 |
| 出版日期 | 2024-03-01 |
| 卷号 | 167页码:16 |
| 关键词 | Landslide susceptibility assessment Deep learning Deep belief network Frequency ratio Intelligent optimization algorithm |
| ISSN号 | 0266-352X |
| DOI | 10.1016/j.compgeo.2024.106106 |
| 英文摘要 | This research proposed a novel deep learning framework that combines the Laplace function sparse regularized continuous deep belief network (LSCDBN) and the Gray Wolf Optimization Algorithm (GWO) and the Whale Optimization Algorithm (WOA), to perform landslide susceptibility assessment. This framework mitigates the challenges of feature homogenization for continuous input variables for landslide condition factors, limited landslide samples, and local optima in the training process. To facilitate this investigation, a meticulous compilation of existing landslide occurrences was used to create a database comprising 18 landslide conditioning factors. To compare the performance of the model, a set of statistical indicators was employed. The results demonstrate the superior performance of both the LSCDBN-GWO model (AUC = 0.952, RMSE = 0.182) and LSCDBN-WOA model (AUC = 0.964, RMSE = 0.174) when compared to the alone LSCDBN model (AUC = 0.913, RMSE = 0.291). It is noteworthy that the performance of the LSCDBN model outperformed that of lone machine learning models (SVM, BP, RF, and LR), lone deep learning models (RNN and CNN), and hybrid deep learning models (CNN-GWO and CNN-WOA). Therefore, it is evident that the proposed LSCDBN-WOA framework can generate models that are optimally suited for landslide susceptibility assessment. |
| 资助项目 | National Key Research and Development Program of China[2023YFC3008300] ; National Key Research and Development Program of China[2019YFC1509702] ; National Natural Science Foundation of China[62273262] ; National Natural Science Foundation of China[42061160480] ; National Natural Science Foundation of China[U23A2044] ; National Natural Science Foundation of China[42071010] ; Shanghai Science and Technology Commission Project[21ZR1466400] |
| WOS研究方向 | Computer Science ; Engineering ; Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001174965600001 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/40816]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Peng, Ming |
| 作者单位 | 1.Tongji Univ, Key Lab Geotech & Underground Engn, Minist Educ, Shanghai 200092, Peoples R China 2.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 3.Tongji Univ, Shanghai Res Inst Intelligent Autonomous Syst, Shanghai 200092, Peoples R China 4.Tongji Univ, Coll Civil Engn, Dept Geotech Engn, Shanghai 200092, Peoples R China |
| 推荐引用方式 GB/T 7714 | Meng, Shaoqiang,Shi, Zhenming,Li, Gang,et al. A novel deep learning framework for landslide susceptibility assessment using improved deep belief networks with the intelligent optimization algorithm[J]. COMPUTERS AND GEOTECHNICS,2024,167:16. |
| APA | Meng, Shaoqiang.,Shi, Zhenming.,Li, Gang.,Peng, Ming.,Liu, Liu.,...&Zhou, Changshi.(2024).A novel deep learning framework for landslide susceptibility assessment using improved deep belief networks with the intelligent optimization algorithm.COMPUTERS AND GEOTECHNICS,167,16. |
| MLA | Meng, Shaoqiang,et al."A novel deep learning framework for landslide susceptibility assessment using improved deep belief networks with the intelligent optimization algorithm".COMPUTERS AND GEOTECHNICS 167(2024):16. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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