American International Journal of Research and Innovation

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Artificial Intelligence Applications in Climate Risk Prediction

Author(s) Prof. Guokun Dai
Country United States
Abstract Climate change has introduced complex environmental uncertainties that significantly affect ecosystems, economies, infrastructure, and human societies. Accurate prediction of climate-related risks has become increasingly important for developing effective adaptation and mitigation strategies. Traditional climate prediction approaches often face limitations due to the complexity of environmental systems, large-scale data requirements, and nonlinear interactions among climatic variables. Artificial Intelligence (AI) has emerged as a transformative technology capable of improving climate risk prediction through advanced data analysis, pattern recognition, machine learning models, and predictive analytics.
This study examines the applications of artificial intelligence in climate risk prediction by analysing the role of machine learning, deep learning, remote sensing analytics, and intelligent forecasting systems. The research adopts a conceptual analytical framework based on existing scientific literature, AI-based climate modelling approaches, and sustainability research. The analysis focuses on how AI techniques improve prediction accuracy, early warning systems, climate modelling, and decision-making processes.
The study identifies that AI-driven climate prediction systems provide significant advantages in managing climate uncertainties by processing large environmental datasets and detecting complex climate patterns. However, challenges related to data quality, computational requirements, model transparency, ethical considerations, and technological accessibility remain important barriers. The findings suggest that effective integration of AI with climate science can strengthen climate resilience, improve disaster preparedness, and support evidence-based environmental decision-making.
Keywords Artificial Intelligence, Climate Risk Prediction, Machine Learning, Deep Learning, Climate Modelling, Environmental Data Analytics, Climate Adaptation
Field Engineering
Published In Volume 4, Issue 3, May-June 2022
Published On 2022-05-15
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