Prediction of Occurrence of Extreme Events using Machine Learning

2021/10/11 by J. Meiyazhagan, S. Sudharsan, Meiyazhagan, J. +5 · 1 citation
Computer Science · Physics and Astronomy · #Anomaly Detection Techniques and Applications #Chaotic Dynamics (nlin.CD) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #cs.LG #nlin.CD

paper · pdf · doi:10.48550/arxiv.2110.09304

To appear in The European Physical Journal Plus

openalex publication_date 2021/10/11 · arxiv created 2021/12/02 · arxiv updated 2021/12/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

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.

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• Extreme events in MLC circuit

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