Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition

Chenhui Wang, Gaocong Lin, Cuiqiong Zhou (), Wei Guo and Qingjia Meng
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Chenhui Wang: Technology Innovation Center for Geological Environment Monitoring, MNR, Baoding 071051, China
Gaocong Lin: Technology Innovation Center for Geological Environment Monitoring, MNR, Baoding 071051, China
Cuiqiong Zhou: Key Laboratory of Geohazard Forecast and Geoecological Restoration in Plateau Mountainous Area, MNR, Kunming 650216, China
Wei Guo: Technology Innovation Center for Geological Environment Monitoring, MNR, Baoding 071051, China
Qingjia Meng: Technology Innovation Center for Geological Environment Monitoring, MNR, Baoding 071051, China

Land, 2024, vol. 13, issue 10, 1-17

Abstract: 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.

Keywords: displacement prediction; kernel extreme learning machine; variational mode decomposition; three gorges reservoir area; influencing factors (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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