IoT Water Management and Distribution System for Smart City using Artificial Intelligence - Meena, K. Bharathi; Dhanalakshmi, S. (Journal of Ubiquitous Computing and Communication Technologies, 2023)
Year after year, the accessible percentage of the world's freshwater resources decreases. According to a World Economic Forum assessment, increased water consumption will result in catastrophic global shortages during the next two decades. The majority of water loss occurs during distribution in pipes during shipment. A water distribution system" using the Internet of Things (IoT) combining "cloud and fog computing" is presented for water distribution and underground pipeline health monitoring to remove the loss. Consumer needs must be assessed to design an effective IoT-based water supply system. As a result, a deep learning method known as Long short-term memory (LSTM) is suggested to estimate customer water consumption. An integrated IoT "Water Distribution Network (WDN)" is being created by employing hydraulic engineering and more accurate demand forecasts. The WDN design will minimize transport losses while maintaining water quality for users. This will result in the creation of smart systems for distributing water. Finally, the suggested algorithms' performance is analyzed and compared. The results demonstrate that the water distribution system with intelligence can effectively monitor the network.
Keywords: Water Distribution Network; Internet of Things; Long Short-Term Memory; Water Management; Smart City