Deep Learning Approaches for Forecasting Water Demand in IoT-Based Water Systems

The efficient use of water has become vital role in today's globe because of the increasing needs of urbanization and climate change,. The application of deep learning, namely Long Short-Term Memory (LSTM) networks, to the prediction of water demand in Internet of Things (IoT)-based water distribution systems is examined. Our goal is to increase the precision and dependability of water demand forecasts by utilizing real-time data gathered from Internet of Things sensors and then analyzing it using innovative artificial intelligence models. This paper introduces a Hybrid Multichannel Model that forecasts water demand and predicts water quality by combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The model gathers real-time data on water use and environmental conditions by exploiting Internet of Things (IoT) sensors. CNNs are used to identify spatial patterns, and RNNs more especially Long Short-Term Memory (LSTM) networks are used to record temporal dependencies. By combining these two methods, predictive accuracy is increased, allowing for accurate forecasting of water demand and prompt detection of problems with water quality. Reducing water waste and guaranteeing a sustainable and high-quality water supply are made possible by the model's real-time processing and analysis of massive amounts of data.