Machine Learning and Predictive Analytics for Climate Change Impact Assessment

Abstract

Climate change poses unprecedented global challenges that require intelligent data-driven solutions for mitigation and adaptation. This paper investigates how predictive analytics powered by machine learning can be used to assess and forecast the impact of climate change on ecosystems, agriculture, and human societies. Large-scale datasets from satellite imagery, weather records, and environmental sensors are analyzed to predict extreme weather events, rising sea levels, and shifts in agricultural productivity. Advanced predictive models including random forests, support vector machines, and deep neural networks are reviewed for their capacity to handle heterogeneous environmental data. The paper also emphasizes the role of AI in enhancing disaster preparedness, sustainable farming practices, and policy-making through evidence-based predictions. The challenges of data uncertainty, computational complexity, and ethical considerations in environmental data usage are critically examined to provide a comprehensive overview. KEYWORDS: Predictive analytics, Climate change, Machine learning,Environmental data, Sustainability