https://doi.org/10.1140/epjp/s13360-021-02249-3
Regular Article
Prediction of occurrence of extreme events using machine learning
1
Department of Nonlinear Dynamics, Bharathidasan University, 620 024, Tiruchirappalli, Tamil Nadu, India
2
PG and Research Department of Physics, Nehru Memorial College (Autonomous), 621 007, Puthanampatti, Tiruchirappalli, Tamil Nadu, India
Received:
12
October
2021
Accepted:
1
December
2021
Published online:
13
December
2021
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.
© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2021