• Krishnav Kumar Gupta

A strong connection exists between water supply heritage resources and the impacts of climate change, which are increasingly being assessed using machine learning techniques. Research indicates that machine learning methods can estimate hydrological water balance under climate change scenarios, forecast managed aquifer recharge sites, and evaluate the impacts on water supply systems. Moreover, integrating machine learning with land cover modeling has proven instrumental in formulating policies aimed at mitigating climate change effects on water resource management. However, the advancement of such applications faces challenges, including data availability and model transferability issues. Addressing these barriers through spatial analysis and community engagement is essential to enhance the resilience and safety of water heritage resources in the face of climate change. Besides this, applying machine learning in the management of water promotes effective decision-making by reducing risk associated with resource scarcity and ecosystem degradation. With improving predictive models and the input of a diversity of sources, these techniques hold a prospect of accurate forecasting in predicting water availability and encouraging proactive conservation strategies. In the end, technological, policy, and community-based approaches can work towards effective solutions in protecting the water heritage from the changes that climate change is continuously imposing.

Climate Change, Water Resources, Machine Learning

"Evaluation of Climate Change Impact on Heritage Water Resources Using Machine Learning Algorithms By Krishnav Gupta", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.g364-g372, December-2024, Available :http://www.jetir.org/papers/JETIR2412642.pdf

"Evaluation of Climate Change Impact on Heritage Water Resources Using Machine Learning Algorithms By Krishnav Gupta", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppg364-g372, December-2024, Available at : http://www.jetir.org/papers/JETIR2412642.pdf