International Journal of Science, Engineering and TechnologyAuthors: Vishwkarma Mohit Santapal, Prof. S. S. Bhor, Prof. M. C. Kolse, Prof. S. K. Said
Abstract: Natural disasters such as floods, earthquakes, cyclones, landslides and forest fires cause severe loss of life, property and environment. Conventional disaster prediction depends on historical records, weather bulletins and expert analysis, and delayed or incomplete information makes response difficult. This paper reviews artificial intelligence and machine learning for disaster prediction and early warning. It examines flood prediction with machine learning and deep learning on rainfall, river level and catchment data, including rainfall–runoff modelling with recurrent networks and global flood forecasting in ungauged basins; precipitation nowcasting with convolutional recurrent and generative models on radar data; earthquake detection and phase picking with deep networks and the scientific limits of earthquake early warning; wildfire risk prediction; and satellite and aerial damage assessment with social-media analysis for post-disaster situational awareness. It discusses data sources, from meteorological services and river gauges to satellite imagery and IoT sensors, and the design of risk dashboards and alerting that follow people-centred early warning principles and India's disaster management framework. A five-layer architecture comprising data acquisition, integration, risk modelling, warning and dissemination, and response support is proposed, with research gaps, objectives, a comparison of hazards and challenges such as data scarcity, rare events, false alarms and last-mile communication. The review stresses that AI predictions supplement, never replace, official warnings.
DOI: https://doi.org/10.5281/zenodo.24217677
