Optimizing Water Distribution Pump Scheduling with SVM-Based Machine Learning Using IoT Sensor Data

Water distribution is essential to urban infrastructure, and pump scheduling is crucial to water reliability. Traditional pump scheduling uses preset or manual modifications, wasting resources and water. This paper introduces a unique strategy that uses Machine Learning (ML) techniques, notably Support Vector Machines (SVM) and Internet of Things (IoT) data for dynamic pump scheduling in water distribution systems to overcome these difficulties. The proposed method uses IoT sensors throughout the water distribution network to gather real-time water flow, pressure, and demand data. After processing, an SVM-based ML model predicts water demand and system behavior using this data. SVM, which can handle non-linear and high-dimensional data, captures complicated IoT variable interactions. The system provides optimum pump schedules in real time using SVM for prediction. These schedules adapt to changing circumstances to maximize energy efficiency and reduce pipeline breakdowns. SVM integration also detects abnormalities and leaks, improving system dependability and decreasing water loss. It advances IoT and ML in water distribution management. The SVM-based strategy improves water distribution network operating efficiency, saving money, reducing environmental impact, and enhancing service quality. The suggested method improves water supply system sustainability and resilience in smart cities and urban areas by being scalable and adaptive.