Application of Machine Learning to Investigate the Impact of Climatic Variables on Marine Fish Landings

Abstract

Paper summary generated by OpenAI: This study explores the application of machine learning (ML) techniques to predict marine fish landings in Malaysia, utilizing a dataset comprising 18 years of climatic variables and fish landing data from five major states. Three ML models—linear regression (LR), decision tree (DT), and random forest (RF)—were employed to analyze the data, with the RF model demonstrating the highest predictive accuracy, achieving R² values of 0.89 in testing and a Nash–Sutcliffe efficiency (NSE) of 0.86. This research represents a pioneering effort to integrate environmental factors into fish landing predictions, providing valuable insights for enhancing food security and fisheries management in Malaysia.