Forecasting of flood events using two-model approach

Floods are one of the most pervasive and costly natural disasters that result in notable socio-economic impacts. Flood forecasting systems are crucial for effective flood risk management, hazard assessment, and decision makings. However, accurate modelling and forecasting of flood events is difficult due to uncertainties in forecasting of meteorological and hydrological input variables. Forecasting is additionally complicated when floods are compound. Conventional flood forecasts typically consider only one driver at a time, whether it is ocean, fluvial or pluvial without considering the often compound nature of flood events. In this study, a novel two-step approach to forecasting of compound coastal-fluvial floods was developed. The two-model hydrodynamic-machine learning forecasting system combines and links the hydrodynamic model with machine learning (ML) model, where the hydrodynamic outputs representing various probability flood events are used to train the ML algorithm in order to predict the inundation patterns resulting from a combination of coastal and fluvial flood drivers occurring simultaneously. In step one, the MSNFlood model was used to simulate the compound coastal-fluvial flooding over coastal city of Cork, Ireland. In step two, the hydrodynamic model outputs such as time variable water depths across urban floodsplains were used to train the ML model. In total 7 ML models (Support Vector Regression (SVR), Support Vector Machine (SVM), Radial Basis Function (RBF), Linear Regression (LR), Gaussian Process Regression (GPR), Decision Tree (DT), and Artificial Neural Network (ANN) ) were applied in this study. Results show that the ML models, when trained on accurate hydrodynamic model outputs, can provide reliable estimates of flood inundation and associated water depths. Three ML models: RBF, ANN and DT show particularly strong performance and are found suitable for flood forecasting. In overall, the novel coupled hydrodynamic-ML system can be successfully used for flood forecasting.