STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Water Resource Engineering and Management Original Research

Artificial Intelligence for Real-time Water Management

  1. Yamini N. Deshvena Civil Engineering Department, Shri Shivji Institute of Engineering & Management Studies, Parbhani, Maharashtra, India
  2. Sushmita M. Deshpande Civil Engineering Department, Shreeyash College of Engineering and Technology, Parbhani, Maharashtra, Indi

Abstract

Effective water management is vital for sustainable development, requiring the strategic allocation and utilization of water resources to satisfy the diverse demands of agriculture, industry, and households. Traditional methods are increasingly inadequate due to escalating challenges from climate change and population growth, which amplify water scarcity and distribution issues. To overcome these challenges, we need innovative solutions. Artificial intelligence offers significant potential in revolutionizing realtime water management through advanced techniques such as predictive analytics, optimization algorithms, and automated decision-making. This abstract explores how artificial intelligence technologies, such as machine learning, neural networks, and data-driven models, are used to develop advanced water management systems. By integrating artificial intelligence with Internet of Things sensors and remote sensing technologies, these systems provide continuous, real-time monitoring of water quality and availability. These technologies can forecast demand and supply variations, allowing for more efficient and effective management of water distribution networks. Furthermore, artificial intelligence–driven systems optimize water resource allocation, ensuring that each sector receives the necessary amount without wastage. These systems excel at identifying and responding to emerging issues, such as potential droughts or floods, allowing for proactive mitigation. Numerous case studies demonstrate the practical benefits of artificial intelligence in water management. For example, artificial intelligence has been effectively used to reduce water wastage through precise leak detection and repair, improve irrigation efficiency by adjusting water delivery based on real-time soil moisture data, and mitigate the impacts of extreme weather events through predictive modeling. In agriculture, artificial intelligence-driven irrigation systems have significantly enhanced crop yields by providing optimal watering schedules. In urban environments, artificial intelligence has helped manage stormwater and reduce flooding risks by optimizing drainage systems based on weather forecasts. Adopting artificial intelligence-driven water management systems not only promotes sustainable resource usage but also strengthens resilience to environmental changes, supporting global water security goals. By leveraging artificial intelligence technologies, we can address the limitations of traditional water management approaches and develop more robust, adaptive, and efficient systems that contribute to sustainable development and environmental stewardship.

Keywords

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