Issue |
E3S Web Conf.
Volume 552, 2024
16th International Conference on Materials Processing and Characterization (ICMPC 2024)
|
|
---|---|---|
Article Number | 01074 | |
Number of page(s) | 14 | |
DOI | https://doi.org/10.1051/e3sconf/202455201074 | |
Published online | 23 July 2024 |
Analytical Survey on the Sustainable Advancements in Water and Hydrology Resources with AI Implications for a Resilient Future
1 Department of Mechanical and Industrial Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India.
2 Institute of Aeronautical Engineering, Hyderabad, Telangana, India.
3 Master of Computer Application, New Horizon College of Engineering, Bangalore, India.
4 Lovely Professional University, Phagwara, India.
5 Radiology Techniques Department, College of Medical Technology, The Islamic University, Najaf, Iraq.
6 Lloyd Institute of Engineering & Technology, Greater Noida, Uttar Pradesh, India.
* Corresponding Author: alok.bhadauria@manipal.edu
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.
Key words: Deep learning / machine learning / Convolutional Neural Network / aquatic life / water bodies / sustainability
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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