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Corresponding Author ORCID Identifer

Naveen Palanichamyhttps://orcid.org/0000-0003-4601-9770

Section

Regular Issue

Abstract

Climate change presents significant challenges to ecosystems, economies, and societies globally. In Malaysia, a tropical country highly dependent on its natural resources, the impacts are evident in altered rainfall patterns, rising temperatures, and extreme weather events. Despite these challenges, many studies still predominantly rely on traditional statistical methods, which limit their capacity for making accurate climate predictions and developing effective policy solutions.This study effectively addresses the existing gap in research by analyzing extensive historical climate data using advanced machine learning (ML) techniques. The primary focus is on accurately forecasting trends in both precipitation patterns and surface air temperature fluctuations. Performance measures like Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are used to assess three ML models: Support Vector Regression (SVR), Random Forest Regression (RFR) and Linear Regression (LR). The findings demonstrate that LR performs better than the other models in forecasting patterns of precipitation and temperature. The results suggest a significant increase in temperature and unpredictable patterns of precipitation, and that poses major implications for agriculture, infrastructure resilience, and water management. Malaysia's climate resilience is improved by this research, which promotes data-driven policymaking by assessing current climate adaptation methods and offering practical ideas.

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