AI for Climate Modeling and Environmental Sustainability
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I3P114Keywords:
Artificial Intelligence, Climate Modeling, Environmental Sustainability, Machine Learning, Deep Learning, Earth System Modeling, Renewable Energy Optimization, Environmental Monitoring, Climate Prediction, Carbon Emissions Reduction, Sustainable Development, Data-Driven Climate ScienceAbstract
Climate change and environmental degradation represent some of the most urgent and complex challenges facing humanity. Rising global temperatures, extreme weather events, biodiversity loss, ocean acidification, and resource depletion demand sophisticated analytical tools capable of understanding interconnected Earth systems. Traditional climate modeling approaches, grounded in physics-based simulations and large-scale numerical methods, have provided invaluable insights but face limitations in computational scalability, uncertainty quantification, and integration of heterogeneous data sources. Artificial intelligence (AI) has emerged as a transformative force in climate science and environmental sustainability by enabling advanced data-driven modeling, predictive analytics, optimization, and decision support. Through machine learning, deep learning, and hybrid AI-physics frameworks, researchers can enhance climate predictions, monitor environmental changes in real time, optimize renewable energy systems, and support sustainable resource management. This article presents a comprehensive and detailed exploration of AI for climate modeling and environmental sustainability, examining theoretical foundations, methodological innovations, computational frameworks, and real-world applications. It further discusses ethical considerations, data governance, interpretability challenges, and the environmental footprint of AI itself. By integrating computational intelligence with environmental science, AI offers powerful tools to accelerate climate research, inform policy decisions, and foster resilient and sustainable systems for the future.
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