Artificial Intelligence for Climate Change Prediction: How Machine Learning Can Improve Environmental Planning
DOI:
https://doi.org/10.47941/je.3668Keywords:
Climate Change, Artificial Intelligence, Machine Learning, Climate Forecasting, Environmental Planning, Sustainability, Deep LearningAbstract
Purpose: The study examines how artificial intelligence can improve climate change prediction and support environmental planning in response to growing global challenges such as rising temperatures, floods, droughts, wildfires, sea-level rise, and ecosystem disruption.
Methodology: The research adopts a qualitative review-based approach supported by evidence from peer-reviewed academic literature, documented case studies, and global climate reports. Foundational machine learning models such as Long Short-Term Memory networks, Random Forests, and deep neural networks were examined alongside recent applications in weather forecasting, flood prediction, wildfire mapping, drought monitoring, and precipitation nowcasting. Secondary data sources including satellite imagery, ERA5 climate reanalysis datasets, and Earth observation platforms were also reviewed to assess their relevance in AI-driven climate analytics.
Findings: The study finds that artificial intelligence significantly improves the speed, scale, and accuracy of climate forecasting compared with many conventional approaches. AI models demonstrate strong performance in medium-range weather prediction, rainfall nowcasting, flood forecasting, drought assessment, wildfire susceptibility mapping, and tropical cyclone monitoring. These capabilities can help governments and organizations reduce disaster losses, allocate resources more efficiently, improve food and water security, and design smarter environmental policies. However, challenges such as data inconsistency, model transparency, computing cost, and sustainability concerns remain important limitations.
Unique Contribution to Theory, Policy, and Practice: This research contributes to theory by presenting an integrated framework linking artificial intelligence with climate prediction and environmental planning. It contributes to policy by highlighting how predictive intelligence can support national adaptation strategies, disaster risk reduction, and sustainable development goals. Practically, it offers decision-makers, researchers, and educational institutions a roadmap for using AI tools to improve resilience planning, environmental governance, and future climate innovation.
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