首页|A comparative study of different machine learning methods for reservoir landslide displacement prediction
A comparative study of different machine learning methods for reservoir landslide displacement prediction
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NSTL
Elsevier
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.
Key Laboratory of Exploration Technologies for Oil and Gas Resources (Yangtze University) Ministry of Education
School of Geosciences Yangtze University
Faculty of Engineering China University of Geosciences
Badong National Observation and Research Station of Geohazards China University of Geosciences
Discipline of Civil Surveying and Environmental Engineering Priority Research Centre for Geotechnical Science and Engineering The University of Newcastle
NSTL
Elsevier
