Flash Flood Forecasting Using Support Vector Regression: An Event Clustering Based Approach
Khaled Boukharouba, Pierre Roussel, Gérard Dreyfus, Anne Johannet

We present a new machine learning approach to flash flood forecasting in the absence of rainfall forecasts, based on the agglomerative hierarchical clustering of flood events. Each cluster contains events whose models have similar behaviors. Specific Support Vector Regression models are then trained from each cluster. The test results show that a specific model may be more accurate than a general model trained from all floods present in the training database.