This study introduces a generic data-driven model for urban hydraulic modelling, designed to estimate flow rates in catchment areas. The model employs a machine learning architecture trained on historical observational data from 30 Water Resource Recovery Facility (WRRF) sites across France, covering diverse hydraulic and environmental conditions. A generic data-driven model integrates principles of machine learning with extensive, varied datasets to create adaptable tools that generalize across different locations and conditions without requiring site-specific recalibration. In this case, the model predicts peak flow curves, enabling the estimation of peak and nominal flow rates over time intervals ranging from 1 hour to 6 months. The database was constructed from observational data measured in the sewage networks and at the inlets of wastewater treatment plants managed by Suez Eau France, supplemented with data on land cover, soil type and rainfall from Open Data sources. The model’s effectiveness was evaluated on a pilot site, validating its versatility. This tool serves as a valuable decision-support resource for engineers and consultants in urban water management. By leveraging machine learning and a robust, diverse dataset, this approach enhances reliability, adaptability, and efficiency in addressing complex urban hydraulic challenges.
Claudio et al. (Mon,) studied this question.