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Mr. Chetan S. Chavan is an Assistant Professor in the Department of Civil Engineering at PCET's Pimpri Chinchwad College of Engineering and Research, Ravet, Pune, Maharashtra, India. He is involved in civil engineering education and academic activities. His contribution focuses on the application of multimodal AI to environmental monitoring and weather-related assessment.
Immediate meteorological evaluation and drought appraisal are crucial for sustainable farming, water resource administration, disaster readiness, and climate adaptability. Traditional meteorological forecasting and drought assessment methods frequently depend on singular data sources, including weather observations, satellite images, or past precipitation records. This section introduces a comprehensive AI system that amalgamates meteorological sensor data, satellite and remote-sensing visuals, soil moisture metrics, hydrological parameters, and past climate archives. Deep learning architectures, such as convolutional neural networks, recurrent neural networks, Transformers, and multimodal fusion frameworks, are analysed for their contributions to feature extraction and the production of weather predictions and drought intensity assessments. The suggested methodology lays the groundwork for astute weather observation and anticipatory drought control, highlighting the necessity for thorough validation across various geographical areas, seasons, and climatic circumstances.