Increased urbanization and population growth are creating problems in urban water distribution systems, which include leakage, imbalance of pressure, energy loss, and poor utilization of resources. There is no monitoring and maintenance in traditional water systems, which has led to large-scale water losses. This research work demonstrates a design of an optimized smart pipe network system for urban water distribution by employing IoT technology and Machine Learning (ML). Sensors based on IoT technology were installed to monitor parameters such as water flow rate, pressure, and water levels in order to collect data for decision-making and analysis purposes. Hydraulic simulation was performed to evaluate the behavior of the network under different demand scenarios, while pre-processing techniques were employed for better data processing. These techniques included normalization, denoising, and filtering of outliers. SVM, LR, DT, and RF algorithms of ML were employed for prediction and optimization. Results showed that the SVM model achieved the best performance with MAE of 1.12 and RMSE of 1.68, demonstrating improved leakage control, pressure management, operational efficiency, and sustainable urban water distribution.
IoT, Urban water distribution systems, ML Algorithm, Smart pipe network systems.
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