A Review of Artificial Intelligence Applications in Water Consumption Management

• Iran, as a country with an arid and semi-arid climate, faces profound challenges in water resources management. An average annual rainfall of less than ۲۵۰ mm, coupled with urban population growth and low agricultural productivity, has led to the consumption of over ۸۰% of renewable water resources. The agricultural sector, accounting for ۸۷% of water usage, and major metropolitan areas with increasing demands are the primary drivers of this crisis. Artificial intelligence, as a leading technology, offers extensive capabilities in predicting, optimizing, and monitoring water consumption. This research examines the role of AI in water consumption management in major Iranian cities such as Tehran, Isfahan, and Mashhad, as well as in the agricultural sector. The research objectives include analyzing AI applications in demand forecasting, leak detection, precision irrigation, water quality monitoring, and loss reduction. The research methodology is based on a review of Persian and international scientific literature, case studies, and machine learning models. Findings indicate that AI can reduce urban water consumption by ۳۰–۴۰% through intelligent demand forecasting systems using neural networks and quality monitoring with anomaly detection algorithms. In agriculture, AI applications leveraging satellite data and the Internet of Things can improve irrigation efficiency by ۲۵–۵۰%, such as through hybrid machine learning models for furrow irrigation in apple orchards. Key challenges include a lack of high-quality data, initial costs, security concerns, and the need for digital infrastructure. This paper emphasizes water resource sustainability and provides recommendations such as government investment, training for citizens and farmers, and the integration of AI into national policies.

کلیدواژه ها:

• Artificial Intelligence ، Water Resources Management ، Iranian Metropolitan Areas ، Smart Agriculture ، Consumption Optimization ، Technological Challenges ، Climate Sustainability