A comparative study of different machine learning methods for reservoir landslide displacement prediction

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 o...

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Bibliographic Details
Published in:Engineering geology Vol. 298; p. 106544
Main Authors: Wang, Yankun, Tang, Huiming, Huang, Jinsong, Wen, Tao, Ma, Junwei, Zhang, Junrong
Format: Journal Article
Language:English
Published: Elsevier B.V 05.03.2022
Subjects:
China
comparative study
Displacement prediction
Extreme learning machine
landslides
Long short-term memory neural network
neural networks
prediction
Reservoir landslide
Support vector machine
support vector machines
China
Long short-term memory neural network
Support vector machine
Extreme learning machine
Reservoir landslide
Displacement prediction
ISSN:0013-7952, 1872-6917
Online Access:Get full text
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Summary: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. •The performances of machine learning methods depend on the selected evaluation index and reservoir landslide case.•Using single prediction accuracy for evaluating the superiority of machine learning methods may be unreliable.•PSO-KELM and PSO-LSSVM are recommended for their superior mean prediction accuracy and prediction stability.
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ISSN:0013-7952
1872-6917
DOI:10.1016/j.enggeo.2022.106544