A novel deep learning framework for landslide susceptibility assessment using improved deep belief networks with the intelligent optimization algorithm
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 framewo...
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| Vydané v: | Computers and geotechnics Ročník 167; s. 106106 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.03.2024
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| ISSN: | 0266-352X, 1873-7633 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 106106 |
| Author | Peng, Ming Li, Gang Meng, Shaoqiang Liu, Liu Shi, Zhenming Zheng, Hongchao Zhou, Changshi |
| Author_xml | – sequence: 1 givenname: Shaoqiang surname: Meng fullname: Meng, Shaoqiang organization: Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 200092, China – sequence: 2 givenname: Zhenming surname: Shi fullname: Shi, Zhenming organization: Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 200092, China – sequence: 3 givenname: Gang surname: Li fullname: Li, Gang organization: Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 200092, China – sequence: 4 givenname: Ming surname: Peng fullname: Peng, Ming email: pengming@tongji.edu.cn organization: Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Tongji University, Shanghai 200092, China – sequence: 5 givenname: Liu surname: Liu fullname: Liu, Liu organization: State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China – sequence: 6 givenname: Hongchao surname: Zheng fullname: Zheng, Hongchao organization: Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Tongji University, Shanghai 200092, China – sequence: 7 givenname: Changshi surname: Zhou fullname: Zhou, Changshi organization: Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 200092, China |
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| Snippet | This research proposed a novel deep learning framework that combines the Laplace function sparse regularized continuous deep belief network (LSCDBN) and the... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 106106 |
| SubjectTerms | Deep belief network Deep learning Frequency ratio Intelligent optimization algorithm Landslide susceptibility assessment |
| Title | A novel deep learning framework for landslide susceptibility assessment using improved deep belief networks with the intelligent optimization algorithm |
| URI | https://dx.doi.org/10.1016/j.compgeo.2024.106106 |
| Volume | 167 |
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