Explainable AI for Rainfall and Flood Prediction

Accurate prediction of rainfall and flood events is essential for effective disaster preparedness and risk mitigation. Machine learning techniques have remarkably improved forecasting accuracy by processing complex environmental data and identifying subtle patterns that traditional methods might overlook. However, the widespread adoption of these advanced models is often hindered by their lack of transparency, limiting trust among meteorologists and decision-makers. This research systematically evaluates a range of predictive models, including both conventional and state-of-the-art machine learning approaches, alongside leading explain-ability techniques. By doing so, the study addresses the critical challenge of balancing predictive accuracy with interpretability, ensuring that meteorological forecasts are not only precise but also understandable and actionable. The integration of explain-able AI methods enhance model transparency and foster greater confidence in automated predictions, ultimately supporting more informed decision-making in weather-related risk management. This work aims to contribute to the development of trustworthy AI systems that empower communities to better prepare for and respond to extreme weather events.