Making waves: Knowledge and data fusion in urban water modelling

Abstract

Mathematical modeling plays a crucial role in understanding and managing urban water systems (UWS), with mechanistic models often serving as the foundation for their design and operations. Despite the wide adoptions, mechanistic models are challenged by the complexity of dynamic processes and high computational demands. Data-driven models bring opportunities to capture system complexities and reduce computational cost, by leveraging the abundant data made available by recent advance in sensor technologies. However, the interpretability and data availability hinder their wider adoption. This paper advocates for a paradigm shift in the application of data-driven models within the context of UWS. Integrating existing mechanistic knowledge into data-driven modeling offers a unique solution that reduces data requirements and enhances model interpretability. The knowledge-informed approach balances model complexity with dataset size, enabling more efficient and interpretable modeling in UWS. Furthermore, the integration of mechanistic and data-driven models offers a more accurate representation of UWS dynamics, addressing lingering uncertainties and advancing modelling capabilities. This paper presents perspectives and conceptual framework on developing and implementing knowledge-informed data-driven modeling, highlighting their potential to improve UWS management in the digital era.

Keywords

• Modelling
• Data-driven
• Machine learning
• Hybrid model
• Urban water systems