Harnessing Data for Flood Prediction: A Systematic Review of Forecasting Techniques and Information Sources

ABSTRACT Flood forecasting has undergone significant development over the past two decades, with researchers exploring a range of innovative approaches to quantify both the hazards and risks associated with flood events across a range of magnitudes and frequencies. Recent advances in computing power and data availability have enhanced prediction capabilities, shifting the field towards more sophisticated, data‐driven techniques. This paper reviews these methodologies, tracing their evolution from classical models to modern data‐driven approaches, while highlighting recent progress in data sources and data integration. Our bibliometric analysis indicates that the number of data‐driven flood modelling approaches reported in the literature has increased by approximately fourfold since 2015, underscoring the accelerating trend in flood forecasting research. The focus is on data‐centric techniques, innovations, and ongoing challenges, with special emphasis on unconventional data sources (e.g., commercial microwave links) and their role in flood forecasting. By reviewing traditional and cutting‐edge methods, the discussion identifies underexplored gaps and emerging opportunities within the existing literature, highlighting the trajectory that this fast‐emerging field is following.