Cruz et al 2026a - Revision history

Scipediacontent at 08:34, 18 March 2026

2026-03-18T08:34:37Z

Scipediacontent at 10:01, 18 February 2026

2026-02-18T10:01:13Z

Scipediacontent: Scipediacontent moved page Review 958392332820 to Cruz et al 2026a

2026-02-18T09:53:09Z

Scipediacontent moved page Review 958392332820 to Cruz et al 2026a

Scipediacontent: Scipediacontent moved page Draft content 817334979 to Review 958392332820

2026-02-18T09:43:18Z

Scipediacontent moved page Draft content 817334979 to Review 958392332820

Scipediacontent: Created page with " == Abstract ==
Efficient management of urban drinking water networks is challenged by population growth, rising consumption, and leakage-related losses. This study presen..."

2026-02-18T09:43:15Z

Created page with " == Abstract == <p>Efficient management of urban drinking water networks is challenged by population growth, rising consumption, and leakage-related losses. This study presen..."

New page

== Abstract ==

<p>Efficient management of urban drinking water networks is challenged by population growth, rising consumption, and leakage-related losses. This study presents a Systematic Literature Review (SLR) following the PRISMA protocol, covering research published between 2015 and 2025 on smart sensors and advanced techniques for leak detection and consumption optimization. From 788 initial records, 40 studies met the inclusion criteria. Findings indicate that acoustic, pressure, fiber-optic, and hybrid sensing enable real-time monitoring and accurate leak localization, with typical error margins between±1% and ±5%, depending on sensor type and hydraulic conditions. A marked shift toward artificial intelligence (AI) and machine learning is observed for optimal sensor placement, event classification, and prediction, achieving >95% accuracy. The cost analysis reveals a direct relationship between technological sophistication and required investment. Overall, integrating smart sensors with AI provides a promising pathway toward more sustainable, efficient, and resilient urban water management.OPEN ACCESS Received: 06/09/2025 Accepted: 30/10/2025 The search strategy was constructed using combinations of keywords and Boolean operators, adjusted for each database to ensure consistency and reproducibility. The searches targeted titles, abstracts, and</p>

== Document ==
<pdf>Media:Draft_content_817334979-9346-document.pdf</pdf>

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 == Abstract ==
-<p>Efficient management of urban drinking water networks is challenged by population growth, rising consumption, and leakage-related losses. This study presents a Systematic Literature Review (SLR) following the PRISMA protocol, covering research published between 2015 and 2025 on smart sensors and advanced techniques for leak detection and consumption optimization. From 788 initial records, 40 studies met the inclusion criteria. Findings indicate that acoustic, pressure, fiber-optic, and hybrid sensing enable real-time monitoring and accurate leak localization, with typical error margins between&plusmn;1% and &plusmn;5%, depending on sensor type and hydraulic conditions. A marked shift toward artificial intelligence (AI) and machine learning is observed for optimal sensor placement, event classification, and prediction, achieving &gt;95% accuracy. The cost analysis reveals a direct relationship between technological sophistication and required investment. Overall, integrating smart sensors with AI provides a promising pathway toward more sustainable, efficient, and resilient urban water management.OPEN ACCESS Received: 06/09/2025 Accepted: 30/10/2025 The search strategy was constructed using combinations of keywords and Boolean operators, adjusted for each database to ensure consistency and reproducibility. The searches targeted titles, abstracts, and</p>
+<p>Efficient management of urban drinking water networks is challenged by population growth, rising consumption, and leakage-related losses. This study presents a Systematic Literature Review (SLR) following the PRISMA protocol, covering research published between 2015 and 2025 on smart sensors and advanced techniques for leak detection and consumption optimization. From 788 initial records, 40 studies met the inclusion criteria. Findings indicate that acoustic, pressure, fiber-optic, and hybrid sensing enable real-time monitoring and accurate leak localization, with typical error margins between&plusmn;1% and &plusmn;5%, depending on sensor type and hydraulic conditions. A marked shift toward artificial intelligence (AI) and machine learning is observed for optimal sensor placement, event classification, and prediction, achieving &gt;95% accuracy. The cost analysis reveals a direct relationship between technological sophistication and required investment. Overall, integrating smart sensors with AI provides a promising pathway toward more sustainable, efficient, and resilient urban water management.OPEN ACCESS Received: 06/09/2025 Accepted: 30/10/2025 </p>
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 == Document ==
 <pdf>Media:Draft_content_817334979-9346-document.pdf</pdf>

← Older revision	Revision as of 09:53, 18 February 2026
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