Explainable artificial intelligence for sustainable urban water systems engineering

Abstract

Explainable Artificial Intelligence (XAI) has potential for revolutionary improvements in operational efficiency, resilience, and decision-making in the engineering of sustainable urban water systems. Presenting cutting-edge approaches in XAI (such as SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-agnostic Explanations), and counterfactual analysis), this review defines the evolution of explainability approaches specifically for hydrological modelling, demand prediction, and leak detection. As an example, the SHAP values have quantified the impact of meteorological variables on urban runoff models, resulting in a 15 % increase in the prediction accuracy. In terms of numbers, XAI applications in water distribution systems have led to up to 20 % savings in energy consumption by optimizing pump schedules based on interpretable machine learning models. Qualitative benefits have included interpretable neural networks for monitoring water quality that detected anomalies and provided transparent contamination alerts that increased stakeholder trust. Examples from cities such as Amsterdam show how XAI is used to improve smart water metering, with reductions of water losses of 12 %. Additionally, XAI has allowed policymakers to assess the influence of climate change on urban drainage networks through transparent visualization of underlying factors. It also addresses some key challenges along with XAI models or frameworks to be scalable and to work with emerging data streams from IoT. This highlights the promise of XAI as a tool to improve sustainable practices in water management by providing a link between highly complex algorithms and watertight management decisions that are easier to implement.