Global Modeling of Major Flood Events

Floods are one of the most common natural disasters, and the rate of flood related disasters has more than doubled since the year 2000. Accurate and timely warnings are critical for mitigating flood risks, especially for major events that can potentially impact thousands. For that, we develop a global early warning system for major flood events, using discharge predictions from the Global Hydrological Model developed by Google. We use agglomerative clustering to cluster discharge predictions at Hydro-ATLAS sub-basins that exceed certain return period thresholds. These extended space and time clusters indicate potential flood events.  A supervised model is then employed to extract clusters associated with a higher risk of major flood events. We use the Dartmouth Flood Observatory (DFO) and GDACS datasets of historical flood events as ground truth for training the classification model. We use various data sources to extract cluster features, such as estimate of affected population, prior event density at the cluster’s location, total drainage area associated with the cluster etc. Our initial experiments show promising results, demonstrating the potential of a machine learning-based early warning system to accurately predict major flood events on a global scale, using global hydrological discharge predictions. While the results are encouraging, we believe that further refinement and validation of the model, coupled with efforts to improve the quality of ground truth data and further cluster features, could increase the accuracy of the system.