Flood susceptibility mapping at the country scale using machine learning approaches

Abstract

AbstractRiver (fluvial), surface water (pluvial) and coastal flooding pose a significant risk to the United Kingdom. Therefore, it is important to assess flood risk particularly as the impacts of flooding are projected to increase due to climate change. Here we present a high resolution combined fluvial and pluvial flood susceptibility map of England. This flood susceptibility model is created by using past flood events and a series of meaningful hydrological parameters to a training machine learning model. We tested the relative performance of different machine learning algorithms, including Classification and Regression Trees, Random Forest and XGBoost and found the XGBoost performed the best, with an area under the receiver operating characteristic ROC Curve (AUC) of 0.93. We also found the model performed well on unseen areas, and we discuss the possibility of extending to regions that has no information on past flood events. Additionally, to aid in understanding what factors may impact flood risk to a particular area, we used Shapley additive explanations which allowed us to investigate the sensitivity of the predicted flood probability to flood factors at a given location.