Optimization of the Kawe DMA Water Network Using Genetic Algorithms: A Pathway to achieve UN SDG 6

Purpose: This study aims to develop a cost-effective and pressure-resilient pipe replacement strategy for the Kawe District Metered Area (DMA) in Dar es Salaam, Tanzania, to improve hydraulic performance amid aging infrastructure and shifting demand in urban water networks. Methodology: A Multi-Objective Genetic Algorithm (MOGA) was used to generate pipe layouts balancing investment cost and hydraulic performance. Optimization and EPANET-based pressure evaluations were executed in Python for integrated modeling and analysis. Alternatives were screened using multi-criteria evaluation based on reliability, cost-efficiency and feasibility. Sensitivity analysis tested robustness under 10% demand growth and 30% cost escalation. Findings: The process yielded 38 Pareto-optimal layouts with nine selected for detailed review. All selected designs maintained minimum pressures above 5 m. The benchmark layout, involving two pipe replacements of total length of 0.409 km, achieved pressures of 9.87 m and 16.29 m at key junctions at a cost of TSh 19.08 million. Under sensitivity scenarios it sustained pressures above 8.66 m and 14.83 m without redesign. Unique Contribution to Theory, Policy and Practice: This study contributes a scalable, pressure-aware planning framework integrating hydraulic modeling with evolutionary optimization. Theoretically, it advances multi-criteria decision-making in water infrastructure design. Practically, it equips utility planners with a data-driven tool for cost-efficient reinforcement of urban water networks. Policy-wise, it supports strategic investment planning aligned with Sustainable Development Goal 6, promoting equitable and resilient water service delivery.