Abstract : In the future, artificial intelligence (AI) will alter business processes and companies which will have the potential to solve important societal issues such as resource scarcity and environmental sustainability among others. The real value of artificial intelligence will not be found in the way it allows civilization to decrease rather than focusing on the intensity of its energy, water, and land use, in the way it supports and promotes good environmental governance practices. Artificial intelligence could be used to model water elements such water quality variables, evapotranspiration and evaporation, sediment, streamflow, rainfall-runoff, and lake or dams water level variations. The modelling of water variables by artificial intelligence is vital step in the water resources management in any aquatic environment. As a result, AI offers some potential research opportunities in the future for the modelling of water parameters. As the world's population continues to increase, the most important consideration is how to create the most of available water supplies. The decision-making skills of artificial intelligence may be used to achieve this optimization and automation. AI-based planning allows agencies and water agencies to better comprehend real-time loss of water and abuse, develop and implement extensive distribution connections, and the production of net revenues as the commercial goal using artificial intelligence. AI has the ability to provide instant remedies while posing no long-term threats to environmental protection. It is necessary to evaluate the influence of AI on the attainment of the Sustainable water resource management system.
Keywords : Artificial intelligence (AI), Water resource management system.
Cite : Singh, M., Mishra, P., Yadav, A., & Gupta, N. (2024). Artificial Intelligence In Water Resource Management Systems (1st ed., pp. 65-79). Noble Science Press. https://doi.org/10.52458/9788197112492.nsp.2024.eb.ch-07
References :
Barry, F.J., 1969. Evolution of the Enforcement Provisions of the Federal Water Pollution Control Act: A Study of the Difficulty in Developing Effective Legislation, The. Mich. L. Rev., 68, p.1103.
Viessman, W., Hammer, M.J., Perez, E.M. and Chadik, P.A., 1998. Water supply and pollution control.
Mustafa, A., Sulaiman, S.O. and Shahooth, S., 2017. Application of QUAL2K for Water Quality Modeling and Management in the lower reach of the Diyala river. Iraqi J. Civ. Eng, 11, pp.66-80.
Sprague Jr, R.H. and Carlson, E.D., 1982. Building effective decision support systems. Prentice Hall Professional Technical Reference.
Chaves, P., 2002. Planning operation of storage reservoir for water quantity and quality. Kyoto: Kyoto University.
Mounce, S.R., Pedraza, C., Jackson, T., Linford, P. and Boxall, J.B., 2015. Cloud based machine learning approaches for leakage assessment and management in smart water networks. Procedia engineering, 119, pp.43-52.
Li, R., Huang, H., Xin, K. and Tao, T., 2015. A review of methods for burst/leakage detection and location in water distribution systems. Water Science and Technology: Water Supply, 15(3), pp.429-441.
Afzaal, H., Farooque, A.A., Abbas, F., Acharya, B. and Esau, T., 2020. Computation of evapotranspiration with artificial intelligence for precision water resource management. Applied Sciences, 10(5), p.1621.
Johnson, L.E., 1986. Water resource management decision support systems. Journal of water resources planning and management, 112(3), pp.308-325.
Chau, K.W., 2004. Knowledge?based system on water?resource management in coastal waters. Water and Environment Journal, 18(1), pp.25-28.
Ali, M., Deo, R.C., Xiang, Y., Li, Y. and Yaseen, Z.M., 2020. Forecasting long-term precipitation for water resource management: a new multi-step data-intelligent modelling approach. Hydrological Sciences Journal, 65(16), pp.2693-2708.
Loucks, D.P. and Fedra, K., 1987. Impact of changing computer technology on hydrologic and water resource modeling. Reviews of Geophysics, 25(2), pp.107-112.
Xiang, X., Li, Q., Khan, S. and Khalaf, O.I., 2021. Urban water resource management for sustainable environment planning using artificial intelligence techniques. Environmental Impact Assessment Review, 86, p.106515.
Sarkar, A. and Pandey, P., 2015. River water quality modelling using artificial neural network technique. Aquatic procedia, 4, pp.1070-1077.
Maier, H.R. and Dandy, G.C., 2000. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental modelling & software, 15(1), pp.101-124.
Wescoat Jr, J.L., White, G.E. and White, G.F., 2003. Water for life: water management and environmental policy. Cambridge University Press.
Falkenmark, M., Lundqvist, J. and Widstrand, C., 1989, November. Macro?scale water scarcity requires micro?scale approaches: Aspects of vulnerability in semi?arid development. In Natural resources forum (Vol. 13, No. 4, pp. 258-267). Oxford, UK: Blackwell Publishing Ltd.
Hoekstra, A.Y., 2013. The water footprint of modern consumer society. Routledge.
Mollinga, P.P. and Gondhalekar, D., 2012. Theorizing structured diversity. An approach to comparative research on water resources management. Retrieved from Nigeria FRO (2004). National Water Policy. Abuja, Nigeria: Federal Government of Nigeria.
Loucks, D.P., Kindler, J. and Fedra, K., 1985. Interactive water resources modeling and model use: an overview. Water Resources Research, 21(2), pp.95-102.
Johnson, L.E., 1986. Water resource management decision support systems. Journal of water resources planning and management, 112(3), pp.308-325.
Cosgriff, G.O., Forte, P.E., Kennedy, M.A., Russell, J.V., Smith, R.D. and West, A.K., 1985. Interactive computer modeling, monitoring, and control of Melbourne's water supply system. Water Resources Research, 21(2), pp.123-129.
Crate, S.A., 2011. A political ecology of “water in mind”: Attributing perceptions in the era of global climate change. Weather, Climate, and Society, 3(3), pp.148-164.
Gleick, P.H. and Palaniappan, M., 2010. Peak water limits to freshwater withdrawal and use. Proceedings of the National Academy of Sciences, 107(25), pp.11155-11162.
Zimmerer, K.S., 2012. Agrobiodiversity and water resources in agricultural landscape evolution (Andean valley irrigation, Bolivia, 1986 to 2008). Biodiversity in agriculture: domestication, evolution, and sustainability. Cambridge University Press, New York, New York, USA. http://dx. doi. org/10.1017/CBO9781139019514, 27, pp.464-474.
Kenney, D.S. ed., 2006. In search of sustainable water management: international lessons for the American West and beyond. Edward Elgar Publishing.
Wescoat Jr., J.L., 2009. Comparative international water research. Journal of Contemporary Water Research & Education, 142, 1–6
