Water Distribution Networks (WDNs) are critical to urban infrastructure, ensuring the delivery of clean water but subject to ageing, environmental challenges, and operational pressures. This study employs a fuzzy logic-based approach using the Mamdani inference system to evaluate risks in Hashtgerd’s WDN. Key risks include contamination in water wells, structural vulnerabilities in tanks, and mechanical failures in pump stations. The findings reveal an overall risk level of 69.1%, with individual contributions of 66.18% from wells, 66.87% from pump sta-tions, and 71.9% from tanks. Recommendations include stricter zoning, improved maintenance, advanced monitoring, and enhanced security.
Computational Risk Assessment in Water Distribution Network / Barzegar, Yas; Barzegar, Atrin; Marrone, Stefano; Verde, Laura; Bellini, Francesco; Pisani, Patrizio. - (2025), pp. 167-174. ( 25th ICCS International Conference Singapore, Singapore Singapore ) [10.1007/978-3-031-97567-7_14].
Computational Risk Assessment in Water Distribution Network
Barzegar, Yas;Barzegar, Atrin;Bellini, Francesco;
2025
Abstract
Water Distribution Networks (WDNs) are critical to urban infrastructure, ensuring the delivery of clean water but subject to ageing, environmental challenges, and operational pressures. This study employs a fuzzy logic-based approach using the Mamdani inference system to evaluate risks in Hashtgerd’s WDN. Key risks include contamination in water wells, structural vulnerabilities in tanks, and mechanical failures in pump stations. The findings reveal an overall risk level of 69.1%, with individual contributions of 66.18% from wells, 66.87% from pump sta-tions, and 71.9% from tanks. Recommendations include stricter zoning, improved maintenance, advanced monitoring, and enhanced security.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
