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Journals / International Journal of Engineering and Geosciences / 2026 / Volume: 11 Issue: 3

Detection of Failure Potential in the Clean Water Network Using Multi-Criteria Decision Making (MCDM) Methods: The Case of Yenişehir, Mersin

Ganime Melike Kanun(AKSARAY UNIVERSITY)0000-0003-0241-6870
Süleyman Sefa Bilgilioğlu(AKSARAY UNIVERSITY)0000-0002-0881-0396
Muzaffer Can İban(MERSIN UNIVERSITY)0000-0002-3341-1338
Pages
—
Publication Date
2026-06-28
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Ultimately, this research presents an effective geospatial decision support tool for municipalities to prioritize maintenance interventions and minimize urban water losses.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Türkiye was once regarded as a country rich in water resources; however, rapid population growth, climate change, and increasing water demand have accelerated the transition toward water scarcity.
📊
Key Findings & Data:Water losses in distribution networks have reached critical levels, with annual averages often exceeding 50%.
💡
Conclusion & Impact:Ultimately, this research presents an effective geospatial decision support tool for municipalities to prioritize maintenance interventions and minimize urban water losses.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:AHP · GIS · Frequency ratio

Abstract

Türkiye was once regarded as a country rich in water resources; however, rapid population growth, climate change, and increasing water demand have accelerated the transition toward water scarcity. Water losses in distribution networks have reached critical levels, with annual averages often exceeding 50%. Mitigating these physical leakages through proactive infrastructure management is therefore essential. The primary aim of this study is to develop a robust spatial susceptibility model for water distribution network failures using an integrated Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) approach. Focusing on the Yenişehir district of Mersin Province, nine environmental and anthropogenic conditioning factors were identified for failure prediction: soil type, lithology, slope, elevation, aspect, building density, population density, lineament density, and distance to roads. These factors were spatially analyzed and weighted by coupling data-driven FR statistics with expert-based AHP evaluations. The developed model was validated against an independent testing dataset comprising 30% of the actual historical failure inventory, achieving a reliable predictive accuracy rate of 92.40%. Ultimately, this research presents an effective geospatial decision support tool for municipalities to prioritize maintenance interventions and minimize urban water losses.

🔖

Cite This Article

Kanun, G. M., Bilgilioğlu, S. S., İban, M. C. (2026). Detection of Failure Potential in the Clean Water Network Using Multi-Criteria Decision Making (MCDM) Methods: The Case of Yenişehir, Mersin. International Journal of Engineering and Geosciences, Volume: 11 Issue: 3. https://doi.org/10.26833/ijeg.1826315

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