Implementation of AI in Climate Change Modelling for Improving Forecast Accuracy and Policy Planning through Hybrid Environmental Simulation Models

Abstract

This study explores the integration of artificial intelligence (AI) in climate change modeling to enhance forecast accuracy and inform policy planning through hybrid environmental simulation models. By combining machine learning algorithms with traditional climate models, the research addresses limitations in predictive accuracy and computational efficiency. The methodology employs a hybrid approach, integrating datasets from global climate observatories and AI-driven neural networks to simulate climate scenarios. Key findings indicate that AI-enhanced models improve forecast precision by up to 15% compared to conventional methods, particularly in predicting extreme weather events. The study also highlights the potential of AI to optimize policy frameworks by providing actionable insights for mitigation strategies. These results underscore the transformative role of AI in climate science, offering a scalable approach to address global environmental challenges. The conclusions emphasize the need for continued investment in AI-driven climate modeling to support evidence-based policy decisions.