Landslide displacement prediction with step-like curve based on convolutional neural network coupled with bi-directional gated recurrent unit optimized by attention mechanism
文献类型：期刊论文
| 作者 | Meng, Shaoqiang2,3,4; Shi, Zhenming2,3,4; Peng, Ming3,4; Li, Gang2; Zheng, Hongchao3,4; Liu, Liu5; Zhang, Limin1 |
| 刊名 | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
 |
| 出版日期 | 2024-07-01 |
| 卷号 | 133页码:21 |
| 关键词 | Landslide monitoring Baishuihe landslide Complementary ensemble empirical mode decomposition with adaptive noise Machine learning Deep learning Nonlinear weighted huber loss function |
| ISSN号 | 0952-1976 |
| DOI | 10.1016/j.engappai.2024.108078 |
| 英文摘要 | This study aims to accurately predict landslide displacement characterized by a step -like displacement curve, resulting from complex interactions among multiple factors, including periodic and variable elements. Specifically, we propose a framework based on a Convolutional Neural Network (CNN) and optimized Bidirectional Gated Recurrent Unit (BiGRU) with an Attention mechanism, designed to forecast landslide displacement with a step -like curve. Initially, landslide displacements are decomposed into periodic and trend terms utilizing complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm. Subsequently, a CNN layer is constructed to extract intricate high -dimensional features, while a BiGRU layer is established to capture temporal dependencies within historical sequences. Notably, an attention mechanism module is introduced to enhance the impact of key information within rainfall, water level, and historical displacement data. Finally, the nonlinear weighted Huber loss function (NLWHL) evaluation method is introduced to assess the accuracy of the model in predicting mutation states. The results demonstrate that the proposed framework exhibited superior accuracy in predicting landslide displacement in comparison to alternative intelligent algorithms. Regarding total displacement, the CNN-BiGRU-Attention model demonstrates superior predictive capabilities, reducing RMSE values by 12.52%, 14.15%, 15.58%, 21.68%, 21.84%, 32.81%, 58.10%, and 53.81% in the test set compared to CNN-BiGRU, CNN-BiLSTM, BiGRU-Attention, SMA-GRU, SMA-LSTM, SMA-SVM, GRU, and LSTM, respectively. Furthermore, the introduction of NLWHL underscores the remarkable accuracy of the proposed framework in forecasting both creep and mutation states. Assessing the displacement mutation state provides the necessary opportunity for early detection and intervention. |
| 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[42061160480] ; National Natural Science Foundation of China[U23A2044] ; National Natural Science Foundation of China[42071010] ; National Natural Science Foundation of China[42207238] ; NSFC/RGC Joint Research Scheme[N_HKUST620/20] ; Shanghai Science and Technology Commission Project[21ZR1466400] |
| WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001187126300001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/40885]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Peng, Ming |
| 作者单位 | 1.Hong Kong Univ Sci & Technol, Dept Civil & Environm Engn, Hong Kong 999077, Peoples R China 2.Tongji Univ, Shanghai Res Inst Intelligent Autonomous Syst, Shanghai 200092, Peoples R China 3.Tongji Univ, Key Lab Geotech & Underground Engn, Minist Educ, Shanghai, Peoples R China 4.Tongji Univ, Coll Civil Engn, Dept Geotech Engn, Shanghai 200092, Peoples R China 5.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Meng, Shaoqiang,Shi, Zhenming,Peng, Ming,et al. Landslide displacement prediction with step-like curve based on convolutional neural network coupled with bi-directional gated recurrent unit optimized by attention mechanism[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2024,133:21. |
| APA | Meng, Shaoqiang.,Shi, Zhenming.,Peng, Ming.,Li, Gang.,Zheng, Hongchao.,...&Zhang, Limin.(2024).Landslide displacement prediction with step-like curve based on convolutional neural network coupled with bi-directional gated recurrent unit optimized by attention mechanism.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,133,21. |
| MLA | Meng, Shaoqiang,et al."Landslide displacement prediction with step-like curve based on convolutional neural network coupled with bi-directional gated recurrent unit optimized by attention mechanism".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 133(2024):21. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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