A deep learning framework for leakage diagnosis and time series water consumption prediction

A smart city water distribution system offers a dependable and trustworthy water supply through smart water meters. To monitor, manage, automate, and control water distribution in a smart city, a smart water grid uses IoT devices like sensors, smart water meters, and controllers together with data analytics. A wealth of information on water pressure, availability, contamination, and water distribution system flaws may be gathered thanks to the smart sensors. Real-time data collection and analysis ensure that losses are kept to a minimum, improving system effectiveness. By imitating the self-learning functions layer and building a data-driven model with the available dataset, deep learning (DL), the most advanced paradigm of artificial neural network (ANN) computing, distinguishes itself from conventional or shallow learning methods. The suggested framework focuses on the creation of an effective deep learning framework with possible applications in data fusion, predictive analysis, the identification of anomalous events from recorded time series data, and the long short-term memory model for water usage prediction.