Dynamic flood mapping using hydrological modeling and machine learning

Abstract

Flooding is one of the most devastating natural hazards around the globe. Having access to abundant sources of data such as Light Detection and Ranging (LiDAR) and satellite images in Geographic Information System (GIS), it is possible to estimate the geospatial extent of floods. Currently, Machine Learning plays an essential role in GIS applications and flood mapping. In this study, the aim was to provide a precise flood model by improving a hydrological model called Height Above Nearest Drainage (HAND) using one of the most robust machine learning algorithms, Random Forest (R.F.). In this study, first, the essential conditioning factors contributing to flooding were identified using optical satellite images as a reference. Then, using the most efficient conditioning factors, an R.F. classifier was trained to predict flooded areas with training data selected using the HAND model. However, since the HAND model has uncertainties in flood mapping, the Random Sample Consensus (RANSAC) paradigm is used along with the essential conditioning factors to remove outliers. Since the proposed method uses the HAND model predictions as pseudo training points, it is called flood mapping using Pseudo Supervised Random Forest (PS-RF). The accuracy of PS-RF for flood extent prediction was tested in 5 different flood events in Fredericton, NB, and one event in Ottawa, ON, confirming that PS-RF improves the flood mapping results of the HAND model without requiring any ground truth training data.