Mathematical Modeling and Machine Learning for Predicting Climate Change Impacts

Abstract

Climate change has emerged as one of the most critical global challenges, affecting ecosystems, agriculture, water resources, biodiversity, and socioeconomic systems. Traditional climate prediction methods based on physical and statistical models often struggle to capture the nonlinear interactions among environmental variables. Recent advances in Machine Learning (ML) and Artificial Intelligence (AI) provide new opportunities for improving climate forecasting accuracy and supporting evidence-based decision-making. This study proposes an Integrated Mathematical Modeling and Machine Learning Framework (IMMLF) for predicting climate change impacts through the combination of climate dynamics, environmental indicators, and data-driven predictive analytics. The framework integrates mathematical climate models, machine learning algorithms, environmental risk assessment, and decision-support mechanisms to improve prediction accuracy and sustainability planning. The proposed approach supports policymakers, researchers, and environmental managers in understanding future climate scenarios and developing effective adaptation strategies.