AN OVERVIEW OF THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN WATER ENGINEERING

Abstract

All aspects of human life must have access to clean water and sanitation. The blend of artificial intelligence and data analysis may offer more effective ways of water treatment and may enhance human life. These developments may be used to identify harmful micro-organisms and other contaminants in water and reduce the impact of wasteful behaviors on various industries. The efficiency of water and precise instructions to direct the provision of sanitation aid may be enhanced through the application of Artificial Intelligence (AI) in the water industry. This article (SDGs) goes into detail about how to measure water variables, how artificial intelligence interacts with itself, and the United Nations' sustainable development goals. Furthermore, the existing water and wastewater treatment procedures, as well as water supply optimization tools and decision-making processes using artificial intelligence in the water business, are all well captured. The study concludes that artificial intelligence will assist in improving the important parameters in water treatment, supply, and general water utilities in the world.

Keywords

• Application
• Artificial intelligence
• Data Science
• Big Data
• water engineering.

References

1. REFERENCES 1. Alam, G., Ihsanullah, I., Naushad, M., & Sillanpää, M. (2022). Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects. Chemical Engineering Journal, 427, 130011. https://doi.org/10.1016/j.cej.2021.130011
2. 2. Al-Daoud, E. (2009). A comparison between three neural network models for classification problems. Journal of Artificial Intelligence, 2(2), 56-64. https://doi.org/10.3923/jai.2009.56.64
3. 3. Ay, M., & Özyıldırım, S. (2018). Artificial intelligence (AI) studies in water resources. Natural and Engineering Sciences, 3(2), 187-195. https://doi.org/10.28978/nesciences.424674
4. 4. Bello, O., Hamam, Y., & Djouani, K. (2014). Modelling and validation of a coagulation chemical dosing unit for water treatment plants. 2014 IEEE Conference on Control Applications (CCA). https://doi.org/10.1109/cca.2014.6981433
5. 5. Chen, M., & Decary, M. (2019). Artificial intelligence in healthcare: An essential guide for health leaders. Healthcare Management Forum, 33(1), 10-18. https://doi.org/10.1177/0840470419873123
6. 6. Cuerda-Correa, E. M., Alexandre-Franco, M. F., & Fernández-González, C. (2019). Advanced oxidation processes for the removal of antibiotics from water. An overview. Water, 12(1), 102. https://doi.org/10.3390/w12010102
7. 7. Dialynas, E., & Diamadopoulos, E. (2008). Integration of immersed membrane ultrafiltration with coagulation and activated carbon adsorption for advanced treatment of municipal wastewater. Desalination, 230(1-3), 113-127. https://doi.org/10.1016/j.desal.2007.11.020
8. 8. Elsevier. (2014). The role of colloidal systems in environmental protection - 1st edition. Elsevier | An Information Analytics Business | Empowering Knowledge. https://www.elsevier.com/books/the-role-of-colloidal-systems-in-environmental-protection/fanun/978-0-444-63283-8

9. 9. Fane, A., Tang, C., & Wang, R. (2011). Membrane technology for water: Microfiltration, ultrafiltration, Nanofiltration, and reverse osmosis. Treatise on Water Science, 301-335. https://doi.org/10.1016/b978-0-444-53199-5.00091-9
10. 10. Gómez-Ramírez, M., & A. Tenorio-Sánchez, S. (2021). Treatment of solid waste containing metals by biological methods. Natural Resources Management and Biological Sciences. https://doi.org/10.5772/intechopen.92211
11. 11. Jenny, H., Wang, Y., Alonso, E. G., & Minguez, R. (2020). Using artificial intelligence for smart water management systems. https://doi.org/10.22617/brf200191-2
12. 12. John McCarthy, Marvin L. Minsky, Nathaniel Rochester, & Claude E. Shannon. (2006). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955. AI Magazine, 27(4).
13. 13. , S., & Roni, K. A. (2021). The existing technology and the application of digital artificial intelligent in the wastewater treatment area: A review paper. Journal of Physics: Conference Series, 1858(1), 012013. https://doi.org/10.1088/1742-6596/1858/1/012013
14. 14. Mishra, S., & Maiti, A. (2020). Biological methodologies for treatment of textile wastewater. Environmental Processes and Management, 77-107. https://doi.org/10.1007/978-3-030-38152-3_6
15. 15. Mahindra, J. J., Garg, U. K., & Gupta, R. (2017). Coagulation-flocculation technologies for arsenic removal -A review. Asian Journal of Research in Chemistry, 10(3), 405. https://doi.org/10.5958/0974-4150.2017.00069.4
16. 16. Pang, Y. L., & Abdullah, A. Z. (2013). Current status of textile industry wastewater management and research progress in Malaysia: A review. CLEAN - Soil, Air, Water, 41(8), 751-764. https://doi.org/10.1002/clen.201000318
17. 17. Prabhakaran, G., Manikandan, M., & Boopathi, M. (2020). Treatment of textile effluents by using natural coagulants. Materials Today: Proceedings, 33, 3000-3004. https://doi.org/10.1016/j.matpr.2020.03.029
18. 18. Tayfur, G. (2017). Modern optimization methods in water resources planning, engineering and management. Water Resources Management, 31(10), 3205-3233. https://doi.org/10.1007/s11269-017-1694-6
19. 19. Ward, F. A. (2007). Decision support for water policy: A review of economic concepts and tools. Water Policy, 9(1), 1-31. https://doi.org/10.2166/wp.2006.053
20. 20. Abbosh O., Nunes P., Savic V., & Moore M. (2017, March 23). The big squeeze: How compression threatens old industries. MIT Sloan Management Review. https://sloanreview.mit.edu/article/the-big-squeeze-how-compression-threatens-old-industries/
21. 21. Mehmood, H., Liao, D., & Mahadeo, K. (2020). A review of artificial intelligence applications to achieve water-related sustainable development goals. 2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G). https://doi.org/10.1109/ai4g50087.2020.9311018
22. 22. Paul A., Jolley C., & Anthony A. (2021, May 3). Reflecting the past, shaping the future: Making AI work for international development. U.S. Agency for International Development. https://www.usaid.gov/digital-development/machine-learning/AI-ML-in-development
23. 23. The quant crunch: How the demand for data science skills is disrupting the job market. (2017). BHEF. https://www.bhef.com/publications/quant-crunch-how-demand-data-science-skills-disrupting-job-market
24. 24. Schrodt, P. A. (2019). Artificial intelligence and international relations: An overview. Artifical Intelligence and International Politics, 9-31. https://doi.org/10.4324/9780429033575-2