A Multi-Stage Optimized Intelligent Framework for Drinking Water Distribution Management.

Abstract With the complexity of urban water distribution networks, it becomes more difficult to ensure efficiency, reliability and adaptability. Despite the improved visibility and availability of data in GIS mapping and smart metering technologies, the majority of current systems continue to use threshold-based alerts, simple clustering, or regression models including K-Means, ARIMA, or PID control, which are not typically able to reflect spatial-temporal variability, detect localized faults, or respond to the dynamics of changing demand. In order to overcome these drawbacks, the given work proposes an AI-based multi-stage model, which incorporates smart decision-making into water supply systems. This starts with a preprocessing stage, during which raw smart meter telemetry and GIS data are cleansed, normalized, and geotag-aligned to provide synchronized high-quality inputs. The Hydro seq Map clustering algorithm is the first algorithm that works based on the graphical clustering with the help of Graph Convolutional Networks and spectral analysis in order to find the structural anomaly and to divide the network into the hydro coherent parts. The information in these clusters and anomaly insights is sent to the second stage, DemandSeqProphet + + Net, a deep sequence-to-sequence forecasting model, with attention mechanisms, which becomes learned to learn temporal consumption patterns and forecast short- and long-term water demand of each zone. The third component, ReinforceOptoAqua, is an optimization algorithm based on reinforcement learning that creates opportunities to manage the operation of valves, manage the pressure areas, and allocate the water dynamically in real-time by using forecasts and detected anomalies. Experimental assessment based on the publicly available dataset in the SWaT (Secure Water Treatment) indicates that the proposed mechanism yields significantly better results than current models, with a 33.2 percent increase in the accuracy of anomaly detection, a 26.5 percent increase in prediction accuracy, and a 29.1 percent decrease in system-wide water losses, thus pointing to the fact that the proposed mechanism can dramatically revolutionize the conventional water supply-related structures into intelligent, autonomous, and resource-efficient devices.