• Rohan Darandale
• Soham Bacchuwar
• Kunal Bhala

The limited availability of water primarily due to population increment, unpredictable rainfall patterns, and climatic changes has posed a major problem in water resources. Many resource managers still rely on conventional approaches based on trend forecasts of water demand and supply, which are imprecise and incapable of facilitating sound resource management. The following is a machine learning-based approach to water demand forecasting and storage management as presented in this paper. The system takes climate and demographic data feeds for real-time information that helps manage water effectively and develop a reservoir forecast plan. Resource management and ecological sustainability are areas that which this work will be useful to stakeholders.

The limited availability of water primarily due to population increment, unpredictable rainfall patterns, and climatic changes has posed a major problem in water resources. Many resource managers still rely on conventional approaches based on trend forecasts of water demand and supply, which are imprecise and incapable of facilitating sound resource management. The following is a machine learning-based approach to water demand forecasting and storage management as presented in this paper. The system takes climate and demographic data feeds for real-time information that helps manage water effectively and develop a reservoir forecast plan. Resource management and ecological sustainability are areas that which this work will be useful to stakeholders.

"Intelligent Approaches to Water Demand Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.l373-l380, April-2025, Available :http://www.jetir.org/papers/JETIR2504B44.pdf

"Intelligent Approaches to Water Demand Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppl373-l380, April-2025, Available at : http://www.jetir.org/papers/JETIR2504B44.pdf