Prediction of occurrence of extreme events using machine learning

• Published in: European physical journal plus Vol. 137; no. 1; p. 16
• Main Authors: Meiyazhagan, J., Sudharsan, S., Venkatesan, A., Senthilvelan, M.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-01-2022; Springer Nature B.V
• Subjects: Academic disciplines; Algorithms; Applied and Technical Physics; Atomic; Classification; Complex Systems; Condensed Matter Physics; Dynamical systems; Feedback; Lasers; Machine learning; Mathematical and Computational Physics; Mechanical systems; Model testing; Molecular; Multilayer perceptrons; Multilayers; Optical and Plasma Physics; Physics; Physics and Astronomy; Regular Article; Support vector machines; Test sets; Theoretical; Velocity
• ISSN: 2190-5444; 2190-5444
• DOI: 10.1140/epjp/s13360-021-02249-3

Machine learning models play a vital role in the prediction task in several fields of study. In this work, we utilize the ability of machine learning algorithms to predict the occurrence of extreme events in a nonlinear mechanical system. Extreme events are rare events that occur ubiquitously in nature. We consider four machine learning models, namely Logistic Regression, Support Vector Machine, Random Forest and Multi-Layer Perceptron in our prediction task. We train these four machine learning models using training set data and compute the performance of each model using the test set data. We show that the Multi-Layer Perceptron model performs better among the four models in the prediction of extreme events in the considered system. The persistent behaviour of the considered machine learning models is cross-checked with randomly shuffled training set and test set data.