An Integrated Explicit Hydrological Routing and Machine Learning Framework for Urban Detention System Design

The rapid expansion of impervious surfaces in urban environments has significantly increased surface runoff and flood risk. Detention basins, implemented as part of Sustainable Urban Drainage Systems (SUDSs), are widely adopted worldwide to control peak discharges and mitigate recurrent flooding. In this study, an explicit flood routing model is applied to simulate the hydraulic behaviour of an urban detention reservoir, offering a computationally efficient alternative to traditional implicit numerical schemes by avoiding iterative solution procedures. In parallel, twenty-eight machine learning (ML) models are evaluated to estimate the percentage reduction in peak discharge required to comply with local regulatory constraints. The proposed framework integrates explicit hydrological routing with data-driven modelling to support decision-making during the design of detention systems. The methodology is applied to an urban catchment in Cartagena, Colombia, comparing an uncontrolled inflow hydrograph (without SUDSs) with an attenuated outflow hydrograph produced by the detention basin. The results demonstrate a substantial reduction in peak discharge and a delay in the time to peak, fully complying with Colombian regulations that require a minimum attenuation of 30%. Among the evaluated ML models, Squared Exponential Gaussian Process Regression achieved the best performance, yielding coefficient of determination (R2) values of 0.999 in both the validation and test sets. The findings confirm the potential of machine learning techniques to quantify peak-flow reduction requirements accurately and to support the planning and design of detention reservoirs in urban environments. The proposed approach constitutes a practical, efficient, and replicable tool for sustainable urban drainage design since the results of this research can be used to design detention pond systems employing ML tools.