Real-Time Water Forecast and Distribution System (WAFADS) for Optimization of Water Consumption

The increasing demand for water resources in arid regions such as the Sultanate of Oman necessitates the development of intelligent, adaptive, and sustainable distribution systems. Traditional water networks depend on static and time-bound mechanisms that fail to accommodate dynamic consumer requirements, often resulting in inefficiency, wastage, and inequitable allocation. This paper presents the design and implementation of a Water Forecast and Distribution System (WAFADS) that integrates IoT-enabled sensors, cloud analytics, and intelligent control logic to provide demand-responsive water delivery. The system employs Raspberry Pi as a microcontroller unit interfaced with solenoid valves and flow sensors, linked through Microsoft Azure and ThingSpeak Cloud for real-time data aggregation and predictive analysis. Users can specify their water requirements in terms of both volume and timing through a web or mobile interface, enabling dynamic control and equitable distribution. The WAFADS model also incorporates AI-based forecasting and analytical tools for predicting regional water demand and optimizing supply schedules. The proposed system significantly enhances water use efficiency, reduces losses due to leakage and over-supply, and ensures user-centered service delivery. Experimental implementation of a prototype demonstrates that WAFADS achieves an overall reduction of water wastage by nearly 70% compared to traditional distribution systems, while aligning with Oman Vision 2040's goals of sustainability and smart infrastructure development.