EGU23-8534, updated on 26 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

ML approaches to flood susceptibility mapping at the country scale

Geoffrey Dawson2, Junaid Butt1, Paolo Fraccaro1, and Anne Jones1
Geoffrey Dawson et al.
  • 1IBM, Research, Warrington, United Kingdom of Great Britain – England, Scotland, Wales (
  • 2IBM Research Europe

Flooding is one of the most costly disasters in the UK, and its impact is projected to increase under climate change. Detailed, accurate and high resolution modelling and mapping of flood hazards are therefore essential to enable climate change adaptation. However, high resolution physics-based flood inundation models are extremely computationally intensive to run, presenting a challenge when mapping flood risk at the country scale, especially when working with ensembles of driving scenarios to account for uncertainty. Furthermore, efficient physical modelling for a target location and/or event required a priori categorisation of dominant flood type (for example fluvial or pluvial), which determines the selection and configuration of appropriate models. In reality, floods at scales beyond a local level are often a combination of multiple flood types. In recent years, machine learning approaches to mapping flood susceptibility have grown in popularity, enabled by large volumes of geospatial and weather/climate data from which explanatory flood factors can be derived. In this study, we develop a pluvial/fluvial flood susceptibility model for England, using high quality open datasets (elevation, land use, soil type, location of water bodies, rainfall) to derive hydrologically-meaningful features, and an open flood inventory dataset to sample flooded/non-flooded points. We train and test the model with grouped cross-validation hyper-parameter tuning for repeated samples of the data on a regular grid, where testing is carried out on unseen grid squares. We discuss the relative performance of different machine learning algorithms, including Random Forest and XG Boost, and assess the computational intensity and scalability of the model across training and inference phases. We also consider the potential of machine learning approaches to provide uncertainty estimates and, via explainable AI techniques, the sensitivity of the predicted flood probability to explanatory flood factors at any given location. Finally, we reflect on the part the modelling approach can play as part of a range of tools to meet the needs of consumers of flood risk information across multiple economic sectors.

How to cite: Dawson, G., Butt, J., Fraccaro, P., and Jones, A.: ML approaches to flood susceptibility mapping at the country scale, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8534,, 2023.