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Debendra Shadangi is a Professor of Practice at the Faculty of Commerce, The ICFAI University Raipur, India. His contribution focuses on Artificial Intelligence and deep learning approaches for weather and climate prediction.
Meteorological forecasting and climatic prediction are essential for agriculture, disaster response, water resource management, transportation, renewable energy production, and public safety. Traditional forecasting methodologies are predominantly dependent on numerical weather prediction (NWP), physical formulas, past data, and atmospheric modelling. Despite the significant precision attained by these methods, they can be resource-intensive and may struggle to depict highly nonlinear, localised, and swiftly evolving atmospheric events. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) offer data-centric methodologies that may discern intricate correlations from extensive datasets encompassing meteorological, satellite, radar, oceanic, and environmental information. This section offers a comprehensive summary of artificial intelligence and deep learning methodologies for meteorological forecasting and climate assessment. A comprehensive AI-based forecasting model is introduced for creating scalable and dependable weather and climate prediction systems.