Review on the use of artificial neural networks to determine the relationship between climate change and the occupancy rates of dams

Year 2024, Volume: 7 Issue: 1, 140 - 147, 31.03.2024

Furkan Demirbaş Emine Elmaslar Özbaş

https://doi.org/10.35208/ert.1340030

Abstract

Climate change has the potential to raise temperatures, alter precipitation patterns, and alter how water resources are distributed globally. The occupancy rates of drinking water supplies may change as a result of these changes. For instance, dwindling water supplies may result from rising temperatures and diminishing precipitation. As a result, the occupancy rates of the reservoirs may drop, making it harder to deliver drinking water. Climate change, however, might highlight regional variations and result in wetter conditions in some places. The occupancy rates in the reservoirs could rise in this scenario. Heavy rains, however, can also result in additional issues like infrastructure damage and floods. Climate change-friendly actions must be taken to manage water supplies in a sustainable manner. In the management of water resources, dams are crucial. It has been observed that when a reliable estimate of a dam's flow is provided, data-based models can produce valuable findings for a variety of hydrological applications. It is obvious that one of the most important problems is the difficulty in getting utility and drinking water as a result of climate change and other things. The purpose of this study is to compile the works that can be offered as a result of the literature review on the impact of climate change on surface water resources and dams, given the importance of this topic. As a result of this study, we can deduce a link between the occupancy levels of the reservoirs used to supply drinking water and climate change. Climate change has the capacity to increase temperatures, modify precipitation patterns, and shift the distribution of water supplies. The relationship between climate change and water supplies is better understood thanks to this study.

Keywords

Amount of water, artificial neural network, climate change, global warming

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