Verifying an improved intermittent water distribution network analysis method based on EPA-SWMM

Modelling Intermittent Water Supply (IWS) presents challenges, as traditional hydraulic methods based on EPANET are often inadequate due to their inability to simulate the network filling process. While EPA-SWMM (EPA's Storm Water Management Model)-based methods enhance IWS analysis, they remain network-specific and lack universal applicability. This study aims to calibrate and verify an improved EPA-SWMM-based model on a 6 m x 5 m laboratory-scale IWS. Experiments were conducted to capture flow rate data from demand nodes under various conditions. The EPA-SWMM model, based on uncontrolled outlets with flow rate varying by pressure, was calibrated using, an automated procedure that integrated the Genetic Algorithm (GA) into the SWMM-toolkit for optimizing minor loss and pipe roughness coefficients. Comparing model results with experimental data demonstrated the model's capability to simulate the laboratory-scale IWS system behaviour. The model was also applied to a real case study, with results closely aligning with field data, affirming its reliability. The proposed IWS modelling method offers a versatile tool for applications, such as design and scenario analysis for tackling IWS challenges and managing IWS systems. Future research should focus on a large-scale laboratory experiment with pressure and flow sensors, considering air presence in the network to mitigate errors.