Explainable artificial intelligence for sustainable urban water systems engineering
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 (Loca...
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| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302500430X |
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| author | Shofia Saghya Infant Sundaram Vickram A Saravanan C M Mathan Muthu Devarajan Yuarajan |
| author_facet | Shofia Saghya Infant Sundaram Vickram A Saravanan C M Mathan Muthu Devarajan Yuarajan |
| author_sort | Shofia Saghya Infant |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-55d173d5d585435b84dcf99be6cc649d |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-55d173d5d585435b84dcf99be6cc649d2025-08-20T02:14:54ZengElsevierResults in Engineering2590-12302025-03-012510434910.1016/j.rineng.2025.104349Explainable artificial intelligence for sustainable urban water systems engineeringShofia Saghya Infant0Sundaram Vickram1A Saravanan2C M Mathan Muthu3Devarajan Yuarajan4Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, IndiaDepartment of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India; Corresponding authors.Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, IndiaDepartment of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, IndiaDepartment of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, IndiaExplainable 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.http://www.sciencedirect.com/science/article/pii/S259012302500430XExplainable artificial intelligence (XAI)Sustainable urban water systemsHydrological modellingWater distribution optimizationSmart water managementMachine learning interpretability |
| spellingShingle | Shofia Saghya Infant Sundaram Vickram A Saravanan C M Mathan Muthu Devarajan Yuarajan Explainable artificial intelligence for sustainable urban water systems engineering Results in Engineering Explainable artificial intelligence (XAI) Sustainable urban water systems Hydrological modelling Water distribution optimization Smart water management Machine learning interpretability |
| title | Explainable artificial intelligence for sustainable urban water systems engineering |
| title_full | Explainable artificial intelligence for sustainable urban water systems engineering |
| title_fullStr | Explainable artificial intelligence for sustainable urban water systems engineering |
| title_full_unstemmed | Explainable artificial intelligence for sustainable urban water systems engineering |
| title_short | Explainable artificial intelligence for sustainable urban water systems engineering |
| title_sort | explainable artificial intelligence for sustainable urban water systems engineering |
| topic | Explainable artificial intelligence (XAI) Sustainable urban water systems Hydrological modelling Water distribution optimization Smart water management Machine learning interpretability |
| url | http://www.sciencedirect.com/science/article/pii/S259012302500430X |
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