Machine-learning Identification of Critical Sub-Basins for Optimized FEWS Design

Flood Early Warning Systems (FEWS) are among the most effective non-structural measures for reducing flood risk, particularly in data-scarce regions with rapid hydrological responses. However, designing efficient FEWS requires balancing forecasting skill with the economic costs of dense monitoring networks. Identifying the most influential observation points is therefore essential for reliable flood forecasting with minimal instrumentation.

In this study, we propose a data-driven framework to identify critical sub-basins whose monitoring provides the greatest benefit for flood early warning. The framework integrates long-term stochastic rainfall simulation, semi-distributed hydrological modeling, machine learning, and feature importance analysis. High-resolution synthetic rainfall time series are generated using a multifractal-based stochastic approach and used to drive a hydrological model, resulting in an extensive virtual database of flood events across multiple sub-basins. Simulated sub-basin discharges are then used as predictors in a Random Forest model to forecast outlet discharge at different lead times.

Feature Importance Measures (FIM) quantify the relative contribution of each sub-basin to flood forecasting performance, enabling identification of a reduced set of hydrologically dominant sub-basins. The methodology is demonstrated in the semi-arid, mountainous Torghabeh River Basin (northeastern Iran), where limited hydrometric infrastructure and short response times pose significant challenges for flood monitoring. Results show that only a subset of sub-basins exerts dominant control on outlet flood response, while many others contribute marginally. The identified influential sub-basins vary with the forecasting lead time, highlighting the importance of tailoring FEWS design to operational objectives.

Overall, the proposed framework offers a flexible approach for optimizing FEWS design, supporting evidence-based decisions on sensor placement and providing new insights into the internal organization of flood-generating processes.