Optimizing Flood Early Warning Sensor Networks Using Hydrological Modeling and Machine Learning: A Semi-Arid Basin Case Study

Flood early warning systems (FEWSs) in data-scarce and rapidly responding catchments require monitoring networks that provide sufficient forecasting information with minimal operational cost. This study evaluates the applicability of a data-driven sensor-placement framework to the semi-arid Torghabeh River Basin in northeastern Iran, where flood-producing tributaries affect the city of Torghabeh and downstream developed areas. A 2000-year stochastic rainfall series was generated to represent a wide range of hydrological conditions and was used to drive a basin-scale HEC-HMS model parameterized from catchment physiographic characteristics and available hydrological information. After applying the adopted flood-event extraction and filtering criteria, 466 relevant synthetic events were retained for the sensor-placement analysis. Sub-basin hydrographs were then used as predictors in a Random Forest forecasting model, while permutation feature importance, SHAP, and derivative-based importance were combined to identify the most informative monitoring locations. For a 1 h lead time, the progressive predictor-selection analysis identified five of 24 candidate sub-basins as a parsimonious configuration associated with the performance plateau, corresponding to an approximately 80% reduction in monitoring locations. A different but similarly parsimonious configuration emerged for a 2 h lead time, demonstrating that optimal sensor placement is lead-time-dependent. These results demonstrate the potential of explainable machine learning tools to support parsimonious FEWS monitoring-network design under the evaluated synthetic modeling conditions, while operational application requires further validation using independent observed hydrological data.