AI-Powered Flood Prediction: Harnessing Explainable AI (XAI)-driven Graph Neural Network (GNN) Integrated with a GRU Model

Nowadays floods have been one of the most dangerous natural hazards which is affecting both densely populated regions and remote areas without any warning. Over the past years though many prediction techniques have been developed thus a major concern is that most models operate like “black boxes,” makes it difficult for authorities to trust their predictions. This study tries to deal with the issue by designing a combined flood-prediction system that combines a Graph Neural Network (GNN) with a GRU-based time-series model. The idea behind this combination is to understand how different water bodies and nearby geographical regions are connected which is done through the GNN, while the GRU deals with the factors like rainfall, river flow, soil moisture, and other water-related factors. Moreover, the model uses AI methods to explain clearly which factors have the most effect on the prediction geographical points, or temporal trends have the highest influence on the predicted flood events. Nowadays this is very important, as authorities mainly demand transparency and ability to understand before trusting on any automated systems during emergencies. Thus this combination helps not only to generate early warnings but also to help authorities understand the prediction. We observe that the experimental results indicate that the proposed system performs better than traditional machine-learning approaches. Thus , the clear understanding given through XAI visualizations help us to find risk zones, making the model more useful for planners, disaster-response teams, and researchers. Overall, this shows that the combined GNNs and GRUs with explainable capability tools helps transparent, and reliable floodprediction solutions in real-world.