Regional Analysis Of Extreme Weather Events Using Deep Learning

Abstract

The increasing frequency and severity of extreme weather events necessitate advanced predictive models that can effectively analyze complex meteorological phenomena. This study conducts a systematic review of 120 peer-reviewed articles to explore the application of deep learning techniques in weather prediction, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review highlights the transformative potential of hybrid deep learning models, which integrate Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture both spatial and temporal dependencies, significantly improving the accuracy of extreme weather forecasting. The study also examines the integration of multi-modal data sources, such as satellite imagery, IoT sensors, and ground-based observations, to enable comprehensive analyses of weather systems. Additionally, the role of Explainable Artificial Intelligence (XAI) in enhancing the interpretability of predictions and fostering stakeholder trust is critically analyzed. Findings reveal that while deep learning approaches offer substantial advancements, challenges related to data quality, computational demands, and resource disparities remain significant. This review underscores the need for global collaboration and innovation to address these limitations, paving the way for more reliable and equitable applications of AI-driven weather forecasting systems.