Modeling river flow for flood forecasting: A case study on the Ter river

Abstract

Floods affect chronically many communities around the world. Their socioeconomic impact increases year-by-year, boosted by global warming and climate change. Combined with long-term preemptive measures, preparatory actions are crucial when floods are imminent. In the last decade, machine learning models have been used to anticipate these hazards. In this work, we model the Ter river (NE Spain), which has historically suffered from floods, using traditional ML models such as K-nearest neighbors, Random forests, XGBoost and Linear regressors. Publicly available river flow and precipitation data was collected from year 2009 to 2021. Our analysis measures the time elapsed between observing a flow rise event at different stations (or heavy rain, for rainfall stations), and use the measured time lags to align the data from the different stations. This information provides increased interpretability to our river flow models and flood forecasters. A thorough evaluation reveals that ML techniques can be used to make short-term predictions of the river flow, even during heavy rain and large flow rise events. Moreover, our flood forecasting system provides valuable interpretable information for setting up timely preparatory actions