AI-Based Predictive Modelling for Optimized Urban Water Distribution and Conservation

¹ Department of CS & IT, Kalinga University, Raipur, India
² New Delhi Institute of Management, New Delhi, India

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Water is an important asset towards socio-economic growth and environmental sustainability. Water Resources (WR) plays a pivotal role in growth, poverty reduction, and fairness. Traditional Water Management (WM) systems maximize the available WR to satisfy conflicting requirements, e.g., surface and groundwater. Climate change, however, worsens WR management difficulties, making them more unpredictable and harder to manage. This paper presents the Adaptive Smart Dynamic Water Resource Planner (ASDWRP), an AI-based model that optimizesan water systems. The system employs a Markov Decision Process (MDP) to dynamically and efficiently predict water demand and resource allocation. The model is capable of improving water use efficiency, minimising wastage, and decision-making because it unites AI technologies with human cognitive ability. The ASDWRP offers an adaptive, flexible method of controlling WR, especially in urban regions where the demand for water is increasing and the resources are scarce. The new adaptive methods in the model include putting limits on the amount of water used and its places of discharge annually, and using the limits to develop sensitivity-based methods of enhancing effective environmental management and planning. An initial analysis demonstrates that ASDWRP implementation causes an overall improvement in the efficacy of finance regions and efficiency of resource management. Model a sustainable WM ecosystem. These results show that AI has the potential to transform urban water systems, enhance sustainability, minimize wastage, and provide access to water for future generations fairly.