Engineering issues in Water sector
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Emmanuel F.Huja, DOGLAS BENJAMINI, Lusajo Mfwango
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31 December 2025
66
24
Optimizing Water Supply Efficiency: Enhanced Management Strategies and Implementation Framework
DOI:
https://doi.org/10.56542/w.jwempo.v2.i2.a4.2025
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Keywords:
Water distribution optimization, predictive modeling, infrastructure deterioration, seasonal variability, tropical coastal systems
Abstract
Despite substantial global infrastructure investments, water distribution systems in tropical coastal urban environments continue to experience losses exceeding 30-40%, significantly above the international benchmark of 15%, primarily due to inadequate existing static optimization models that fail to capture dynamic interdependencies between infrastructure deterioration, environmental factors, and operational parameters. This research addresses this critical knowledge gap by developing an innovative multi-dimensional predictive framework that integrates infrastructure deterioration modeling with real-time pressure dynamics and seasonal variability patterns through advanced machine learning ensemble methods combined with physics-based hydraulic modeling. The methodology employed a mixed-methods data collection approach from the Kunduchi water supply network in Tanzania, incorporating structured surveys of 35 technical staff with quantitative operational measurements, followed by multiple linear regression analysis and rigorous model validation using normality distribution analysis, multicollinearity assessment, and homoscedasticity testing. The developed model explains 84% of efficiency variance through four key predictors: pipe class (? =-15.21), pipe age (? =-0.48), operating pressure (? =4.15), and age-class interaction effects (? = -0.238), revealing that Class C pipes comprising 55.6% of the network account for 76.2% of repairs, while seasonal variations increase water losses by 34-38% during rainy periods. The research advances established water distribution optimization practices by demonstrating quantifiable 18-24% efficiency improvements through integrated system modeling that captures non-linear interdependencies and temporal autocorrelation effects. This approach provides a scalable methodology for achieving SDG 6 targets through enhanced system efficiency rather than solely infrastructure expansion.
