Optimizing water supply efficiency: Enhanced management strategies and implementation framework

Abstract

Despite global infrastructure investments, tropical coastal water distribution systems experience 30-40% losses, exceeding the 15% international benchmark, due to inadequate static optimization models failing to capture dynamic interdependencies between infrastructure deterioration, environmental factors, and operational parameters. This research develops an innovative multi-dimensional predictive framework integrating infrastructure deterioration modeling with real-time pressure dynamics and seasonal patterns using machine learning and hydraulic modeling. The methodology employed a mixed-methods data collection approach from the Kunduchi water supply network, 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 (β =-15.21), pipe age (β =-0.48), operating pressure (β =4.15), and age-class interaction effects (β = -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. Research shows 18-24% efficiency gains via integrated modeling of water systems with non-linear interdependencies. This scalable methodology achieves SDG 6 targets through enhanced system efficiency rather than infrastructure expansion.