Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition
Displacement deformation prediction is critical for landslide disaster monitoring, as a good landslide displacement prediction system helps reduce property losses and casualties. Landslides in the Three Gorges Reservoir Area (TGRA) are affected by precipitation and fluctuations in reservoir water le...
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MDPI AG
2024-10-01
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| author | Chenhui Wang Gaocong Lin Cuiqiong Zhou Wei Guo Qingjia Meng |
| author_facet | Chenhui Wang Gaocong Lin Cuiqiong Zhou Wei Guo Qingjia Meng |
| author_sort | Chenhui Wang |
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| description | Displacement deformation prediction is critical for landslide disaster monitoring, as a good landslide displacement prediction system helps reduce property losses and casualties. Landslides in the Three Gorges Reservoir Area (TGRA) are affected by precipitation and fluctuations in reservoir water level, and displacement deformation shows a step-like curve. Landslide displacement in TGRA is related to its geology and is affected by external factors. Hence, this study proposes a novel landslide displacement prediction model based on variational mode decomposition (VMD) and a Harris Hawk optimized kernel extreme learning machine (HHO-KELM). Specifically, VMD decomposes the measured displacement into trend, periodic, and random components. Then, the influencing factors are also decomposed into periodic and random components. The feature data, with periodic and random data, are input into the training set, and the trend, periodic, and random term components are predicted by HHO-KELM, respectively. Finally, the total predicted displacement is calculated by summing the predicted values of the three components. The accuracy and effectiveness of the prediction model are tested on the Shuizhuyuan landslide in the TGRA, with the results demonstrating that the new model provides satisfactory prediction accuracy without complex parameter settings. Therefore, under the premise of VMD effectively decomposing displacement data, combined with the global optimization ability of the HHO heuristic algorithm and the fast-learning ability of KELM, HHO-KELM can be used for displacement prediction of step-like landslides in the TGRA. |
| format | Article |
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| issn | 2073-445X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-d1c8bc32d59e4316ba7eaf0ed05863272025-08-20T02:10:54ZengMDPI AGLand2073-445X2024-10-011310172410.3390/land13101724Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode DecompositionChenhui Wang0Gaocong Lin1Cuiqiong Zhou2Wei Guo3Qingjia Meng4Technology Innovation Center for Geological Environment Monitoring, MNR, Baoding 071051, ChinaTechnology Innovation Center for Geological Environment Monitoring, MNR, Baoding 071051, ChinaKey Laboratory of Geohazard Forecast and Geoecological Restoration in Plateau Mountainous Area, MNR, Kunming 650216, ChinaTechnology Innovation Center for Geological Environment Monitoring, MNR, Baoding 071051, ChinaTechnology Innovation Center for Geological Environment Monitoring, MNR, Baoding 071051, ChinaDisplacement deformation prediction is critical for landslide disaster monitoring, as a good landslide displacement prediction system helps reduce property losses and casualties. Landslides in the Three Gorges Reservoir Area (TGRA) are affected by precipitation and fluctuations in reservoir water level, and displacement deformation shows a step-like curve. Landslide displacement in TGRA is related to its geology and is affected by external factors. Hence, this study proposes a novel landslide displacement prediction model based on variational mode decomposition (VMD) and a Harris Hawk optimized kernel extreme learning machine (HHO-KELM). Specifically, VMD decomposes the measured displacement into trend, periodic, and random components. Then, the influencing factors are also decomposed into periodic and random components. The feature data, with periodic and random data, are input into the training set, and the trend, periodic, and random term components are predicted by HHO-KELM, respectively. Finally, the total predicted displacement is calculated by summing the predicted values of the three components. The accuracy and effectiveness of the prediction model are tested on the Shuizhuyuan landslide in the TGRA, with the results demonstrating that the new model provides satisfactory prediction accuracy without complex parameter settings. Therefore, under the premise of VMD effectively decomposing displacement data, combined with the global optimization ability of the HHO heuristic algorithm and the fast-learning ability of KELM, HHO-KELM can be used for displacement prediction of step-like landslides in the TGRA.https://www.mdpi.com/2073-445X/13/10/1724displacement predictionkernel extreme learning machinevariational mode decompositionthree gorges reservoir areainfluencing factors |
| spellingShingle | Chenhui Wang Gaocong Lin Cuiqiong Zhou Wei Guo Qingjia Meng Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition Land displacement prediction kernel extreme learning machine variational mode decomposition three gorges reservoir area influencing factors |
| title | Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition |
| title_full | Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition |
| title_fullStr | Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition |
| title_full_unstemmed | Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition |
| title_short | Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition |
| title_sort | landslide displacement prediction using kernel extreme learning machine with harris hawk optimization based on variational mode decomposition |
| topic | displacement prediction kernel extreme learning machine variational mode decomposition three gorges reservoir area influencing factors |
| url | https://www.mdpi.com/2073-445X/13/10/1724 |
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