Real-time Flood Forecasting Model for Taipei City Using IoT Sensor Networks

Urban flooding is intensifying under climate change and rapid urbanization, particularly in densely built metropolitan basins with limited drainage capacity. Taipei City, located within a low-lying alluvial basin and frequently affected by typhoons and short-duration extreme rainfall, experiences recurrent flash flood hazards. Conventional urban flood forecasting systems primarily rely on static rain gauge or satellite precipitation products, which suffer from coarse temporal resolution, sparse spatial coverage, and high forecast latency, constraining effective real-time early warning.

This study develops an IoT-enabled real-time urban flood forecasting framework for Taipei City by assimilating high-frequency rainfall observations into an operational hydrologic-hydraulic modeling chain. The Keelung River basin is selected as a representative urban catchment. Rainfall observations at a 10-minute temporal resolution are retrieved from Taiwan’s Civil IoT SensorThings API and dynamically injected into a HEC-HMS rainfall-runoff model within the HEC-RTS forecasting environment. The hydrologic model employs the SCS Curve Number method for loss estimation, SCS Unit Hydrograph for runoff transformation, linear reservoir baseflow representation, and Muskingum channel routing. Model calibration and validation are conducted using observed discharge data from historical typhoon events.

Model performance is evaluated using Kling-Gupta Efficiency(KGE), Nash-Sutcliffe Efficiency(NSE), RMSE, and percent bias. The system targets a KGE ≥ 0.75 while achieving a minimum 15-minute reduction in warning latency compared to traditional hourly gauge-driven simulations. The simulated discharge hydrographs are designed for coupling with a 2D HEC-RAS hydraulic model to generate urban flood inundation maps, with spatial performance assessed using an IoU threshold of ≥ 0.65.This study demonstrates that assimilating high-frequency IoT rainfall observations into an operational urban flood forecasting framework can significantly reduce warning latency without degrading hydrologic or hydraulic predictive skill.