- Title
- A comparative study of different machine learning methods for reservoir landslide displacement prediction
- Creator
- Wang, Yankun; Tang, Huiming; Huang, Jinsong; Wen, Tao; Ma, Junwei; Zhang, Junrong
- Relation
- Engineering Geology Vol. 298, Issue 5 March 2022, no. 106544
- Publisher Link
- http://dx.doi.org/10.1016/j.enggeo.2022.106544
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2022
- Description
- This paper compares the performance of five popular machine learning methods, namely, particle swarm optimization–extreme learning machine (PSO–ELM), particle swarm optimization–kernel extreme learning machine (PSO–KELM), particle swarm optimization–support vector machine (PSO–SVM), particle swarm optimization–least squares support vector machine (PSO–LSSVM), and long short-term memory neural network (LSTM), in the prediction of reservoir landslide displacement. The Baishuihe, Shuping, and Baijiabao landslides in the Three Gorges reservoir area of China were used for case studies. Cumulative displacement was decomposed into trend displacement and periodic displacement by the Hodrick–Prescott filter. The double exponential smoothing method and the five machine learning methods were used to predict the trend and periodic displacement, respectively. The five machine learning methods are compared in three aspects: highest single prediction accuracy, mean prediction accuracy, and prediction stability. The results show that no method performed the best for all three aspects in the three landslide cases. LSTM and PSO–ELM achieved better single prediction accuracy, but worse mean prediction accuracy and stability. PSO–KELM, PSO–LSSVM, and PSO–SVM always yielded consistent predictions with slight variations. On the whole, PSO–KELM and PSO–LSSVM are recommended for their superior mean prediction accuracy and prediction stability.
- Subject
- displacement prediction; extreme learning machine; long short-term memory neural network; reservoir landslide; support vector machine; SDG 17; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1473859
- Identifier
- uon:49126
- Identifier
- ISSN:0013-7952
- Language
- eng
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