AI-driven water management offers lifeline to resource-constrained settings

AI’s ability to replicate human engineering decisions without requiring high-end computing infrastructure marks a pivotal shift in how water utilities can operate in the Global South. While traditional solutions rely on consultants or licensed software, WS’s modular, open-access structure allows agencies to plan upgrades, model expansions, and meet regulatory standards independently.

Artificial intelligence is now playing a central role in solving chronic water distribution problems in developing regions, according to a new case study conducted in Georgia’s Khelvachauri municipality. Published in the journal Water, the study, titled “AI for Smart Water Solutions in Developing Areas: Case Study in Khelvachauri (Georgia),” details the implementation of an AI-enhanced version of the Water Smart (WS) application - an integrated tool designed to assist small and mid-sized water utilities lacking the technical capacity and financial resources to manage their hydraulic infrastructure efficiently.

Developed by Macs Energy & Water, the enhanced WS platform combines artificial intelligence with EPANET-based hydraulic modelling and GIS features to optimize pressure recovery and pipe diameter management. Applied to Khelvachauri’s potable water system, the AI-powered module addressed persistent supply issues in the southern region of the municipality, where residents frequently experienced inadequate water pressure during peak consumption hours. The optimization process resulted in a minimum 25-meter pressure increase, reduced pipe velocities to under 1.5 meters per second, and improved pumping efficiency by 15 percent. Leakage rates fell by 8 percent, while simulation run times dropped by 35 percent compared to conventional hydraulic modelling.

What makes AI integration critical for developing water utilities?

The study explains that small and mid-sized utilities, particularly in developing areas, typically cannot afford in-house hydraulic engineers or high-end simulation software. When foreign-funded infrastructure projects end and consultants withdraw, local teams are often left without the technical knowledge to maintain or upgrade systems. The Water Smart platform was designed to bridge this knowledge gap by allowing non-experts to perform complex simulations and apply decision-making logic through an intuitive interface. The AI-enhanced module builds on that foundation by replicating expert-level design functions using a Random Forest regressor, a machine learning algorithm capable of predicting optimal pipe diameters based on flow and pressure data.

The software’s AI module functions as a decision-support system, allowing managers to simulate hydraulic behaviors, test different pipe configurations, and automatically adjust network design. The process begins with the extraction of topological and hydraulic data, followed by an initial EPANET simulation to identify areas with pressure and flow problems. The AI model then recommends pipe diameter adjustments based on predefined velocity thresholds and simulates the optimized configuration. This minimizes reliance on human engineering input while delivering solutions that align with international standards for operational reliability and energy efficiency.

How Did AI Improve Water Network Performance in Khelvachauri?

Khelvachauri, located in Georgia’s southwestern Adjara region, had long struggled with low water pressure due to outdated Soviet-era infrastructure, rugged terrain, and seasonal demand surges linked to tourism. While recent upgrades funded by the German Development Bank included physical infrastructure replacement, insufficient planning and modelling had left critical areas underperforming. Specifically, the southern sector of the network suffered from chronic low pressure, especially during peak hours.

To address this, the WS tool was deployed with its new AI-driven pressure recovery module. The model integrated flow and pressure readings, consumption estimates, and digital elevation data. After identifying deficiencies through simulations, it optimized the pipe network to meet demand forecasts up to 2040. The final configuration restored stable pressure levels—maintaining at least 25 meters of water column across previously underperforming areas—and brought pipeline velocities within the acceptable limit of 1.5 meters per second.

The results also indicated a measurable improvement in operational sustainability. Pressure stabilization contributed to reduced non-revenue water losses—both physical leaks and apparent losses from inaccurate metering—and cut pumping energy consumption by 15 percent. These improvements enhance system resilience, reduce maintenance costs, and extend asset life, making the technology especially valuable in resource-constrained contexts.

What Broader Implications Does This Hold for Water Management in Developing Regions?

The study positions the Khelvachauri case as a replicable model for other low-capacity utilities across developing regions. Unlike national-scale smart water initiatives, such as South Korea’s Smart Water Network Management (SWNM) program, which focus on AI-driven leak detection and water quality monitoring, the WS platform emphasizes pressure optimization and network sustainability for small utilities. Its compatibility with EPANET - a widely used, open-access hydraulic simulation toolkit - ensures that existing data and workflows can be easily integrated.

AI’s ability to replicate human engineering decisions without requiring high-end computing infrastructure marks a pivotal shift in how water utilities can operate in the Global South. While traditional solutions rely on consultants or licensed software, WS’s modular, open-access structure allows agencies to plan upgrades, model expansions, and meet regulatory standards independently. The study also highlights the cost savings associated with reduced dependency on external experts, arguing that democratizing access to AI-enhanced decision-making tools is critical for sustainable water development.

Future modules of Water Smart are already in development and will include real-time leak detection, renewable energy optimization for pumping, and predictive analytics to prepare for climate-related demand shifts. The authors suggest that as cities in the developing world face escalating urbanization and water stress, low-cost, scalable solutions like WS will be instrumental in ensuring water security.