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Water, as an indispensable element for all life forms, plays a crucial role in sustaining ecosystems and fostering biodiversity. Ensuring sustainability in water management practices is paramount to maintaining the delicate balance of nature. It acts as a medium for the movement of nutrients and waste products, metabolic reactions, and the preservation of cell structure. Since it can dissolve a large variety of things, water is frequently referred to as the universal solvent and is necessary for a variety of biological and chemical processes. The paper offers a thorough analysis of the most recent machine learning techniques applied to generation, prediction, enhancement, and classification work in the water sector, with a focus on sustainability. It also acts as a manual for leveraging existing deep learning techniques to address upcoming problems pertaining to water resources while ensuring long-term environmental sustainability. The ethical considerations surrounding the use of these technologies in water resource management and governance, as well as other important topics and concerns, are covered. Lastly, we offer suggestions and future possibilities for the use of machine learning models in sustainable water resources and hydrology. |
