Proactive Hydrological Forecasting and Intelligent Decision Support in Human-Regulated Urban Catchments

Urban catchments are highly human-influenced hydrological systems in which drainage networks, pumping stations, and engineered conveyance structures fundamentally modify runoff generation and flow dynamics. These anthropogenic controls introduce strong nonlinearity and non-stationarity, challenging short-term hydrological forecasting and reducing the effectiveness of reactive flood control, particularly under intensifying extreme rainfall. This study develops an Intelligent Flood Control Decision Support System (IFCDSS) that integrates data-driven hydrological forecasting with adaptive operational control to support proactive urban flood management. At the catchment scale, short-term flood inundation nowcasting is achieved by combining Principal Component Analysis (PCA) and Self-Organizing Maps (SOM) with Nonlinear Autoregressive models with exogenous inputs (NARX). This approach enables efficient extraction of dominant inundation patterns from high-resolution two-dimensional flood maps and provides reliable multi-step-ahead forecasts at 10-minute resolution up to one hour. At the infrastructure scale, hybrid deep learning models (CNN–BP) are used to generate multi-input, multi-output forecasts of sewer, forebay, and river water levels, achieving high predictive skill under rapidly evolving rainfall and operational conditions. Forecast outputs are translated into operational decisions through a decision layer integrating NSGA-III for multi-objective optimization, TOPSIS for solution ranking, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) for real-time pump control. Application to a major pumping-station catchment in Taipei, Taiwan, demonstrates that the system delivers actionable forecasts and control strategies within seconds. Compared with manual operation, the IFCDSS achieves more robust trade-offs among flood mitigation, energy efficiency, and operational reliability. The results highlight the importance of explicitly representing human interventions in urban hydrological forecasting and demonstrate how intelligent decision support can enhance flood preparedness in complex, human-regulated catchments under climate change.