Leveraging Urban Water Distribution Systems with Smart Sensors for Sustainable Cities

Area of Science:

• Environmental Engineering
• Computer Science
• Water Resource Management

Background:

• Urban water distribution systems face challenges like economic losses, water wastage, and resource access gaps, exacerbated by climate change and drought.
• Optimization is crucial for sustainability and efficient resource management in water networks.

Purpose of the Study:

• To apply advanced artificial intelligence (AI) and Internet of Things (IoT) techniques for optimizing urban water distribution networks.
• To quantify the potential water savings and system efficiency improvements using simulation.

Main Methods:

• Utilized EPANET for hydraulic modeling of water networks.
• Employed a linear regression-based algorithm for optimization.
• Integrated AI and IoT for simulation-based network optimization.

Main Results:

• Achieved up to 96.62% system efficiency.
• Obtained a mean absolute error of 0.049, indicating high accuracy.
• Demonstrated potential daily water conservation of up to 648,000 L at high-demand nodes.

Conclusions:

• AI and IoT offer a powerful solution for optimizing urban water distribution systems.
• The proposed approach significantly reduces water wastage and improves resource management.
• Future research should focus on deploying the proposed global architecture in real-world urban water systems.