Climate Change Analysis and Forecasting Using Machine Learning Techniques

Abstract

Climate change is one of the most pressing global challenges, with significant impacts on ecosystems, agriculture, human health, and the economy. Accurate analysis and prediction of climate patterns are essential for effective mitigation and adaptation strategies. This study explores the application of machine learning and data analytics techniques for analyzing climate change trends, with a focus on temperature variation, rainfall patterns, and greenhouse gas emissions. Using publicly available datasets from NASA and NOAA, we extract key climate indicators and apply models such as Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) networks for trend analysis and forecasting. The results demonstrate that data-driven approaches can uncover hidden patterns, improve prediction accuracy, and offer localized insights into changing climatic conditions. The study highlights the potential of integrating artificial intelligence with environmental science to enhance decision-making for climate resilience and sustainability.