A Machine Learning Approach for Forecasting and Visualizing Flood Inundation Information

This paper presents a new data-driven modelling framework for forecasting probabilistic flood inundation maps for real-time applications. The proposed end-to-end (rainfall-inundation) method combines a suite of machine learning (ML) algorithms to forecast discharge and deliver probabilistic flood inundation maps with a 3 h lead time. To classify wet/dry cells, the method applies rainfall-discharge models based on random forest technique on top of classifiers based on multi-layer perceptron. The hybrid modelling framework was tested using two subsets of data created from an observed fluvial flood event in a small flood-prone town in the UK. The results showed that the model can effectively emulate the outcomes of a hydrodynamic model (Flood Modeller (FM)) with considerably high accuracy measured in terms of flood arrival time error and classification accuracy. The mean arrival time difference between the proposed model and the hydrodynamic model was 1 h 53 min. The classification accuracy was measured against a synthetic aperture radar image, producing accuracies of 88.22% and 86.58% for the proposed data-driven model and FM, respectively. The key features of the proposed modelling framework are that it is simple to implement, detects flooded cells effectively and substantially reduces computational time.