,
Kalinga University , Raipur , India
Kalinga University , Raipur , India
This article introduces a cutting-edge solution, called the Hybrid AI-Driven Water Management System, to solve the critical issues of managing water resources in urban India. Most major Indian cities suffer from an increase in demand for water, poorly managed pipe networks, and an excessive amount of water being wasted due to leaks and other outdated pipe infrastructure. The system combines artificial intelligence (AI) techniques, including machine learning (ML) and optimization, with data from various kinds of sensors (IoT), such as temperature, humidity, pressure, etc., that have been installed on the water pipes of urban water distribution systems. The Hybrid model employs a Long Short-Term Memory (LSTM) Algorithm to predict real-time demand surge events and uses Reinforcement Learning to dynamically optimize water distributions with respect to minimization of losses. Additionally, this hybrid approach combines predictive analytics with real-time measured data processing which allows better allocation of resources, increases operational efficiency, and provides more accurate predictions through advanced modeling techniques. The key performance measures (Mean Absolute Error — MAE; RMSE) demonstrate that the Hybrid AI Model performed significantly better than traditional models on average with an MAE of 0.18 & RMSE of 0.22 respectively. The Hybrid Model also proved to reduce water loss. Through more intelligent usage of the IoT based real-time sensor data, Autonomous Water Management was achieved by eliminating human oversight/management through Autonomous Water Usage Strategy Development, effectively reducing overall operational cost thru cost reductions associated with time saved and human labor utilized for monitoring pipe networks. The proposed system is designed to help cities make the transition from inefficient water management systems to sustainable, efficient, and cost-effective systems.
This is an open access article distributed under the Creative Commons Attribution Non-Commercial License (CC BY-NC) License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.