Review of Artificial Intelligence Applications in Dams and Water Resources: Current Trends and Future Directions

Authors

  • Layth Abdulameer Petroleum Engineering Department, University of Kerbala, Karbala 56001, Iraq
  • Najah Mahdi Lateef Maimuri Building and Construction Technologies Engineering Department, College of Engineering and Engineering Technologies, Al-Mustaqbal University, Babylon, Hillah, 51001, Iraq
  • Ala Hassan Nama Water Resources Department/ College of Engineering/ University of Baghdad, Baghdad, Iraq
  • Farhan Lafta Rashid Petroleum Engineering Department, University of Kerbala, Karbala 56001, Iraq
  • Hayder Ibrahim Mohammed Department of Physics, College of Education, University of Garmian, Kalar 46021, Iraq
  • Ahmed Noori Ghani Al-Dujaili Amirkabir University of Technology/ Petroleum Engineering Department, No. 350, Hafez Ave, Valiasr Square, Tehran, 1591634311 Iran

DOI:

https://doi.org/10.37934/arfmts.128.2.205225

Keywords:

Predictive analytics, machine learning, real-time monitoring, data integration, climate adaptation

Abstract

This paper aims to analyze how AI can revolutionize dam and water resource management, the problem areas such as climate change, growing population rate, and deterioration of infrastructure; these AI technologies, in turn, drive predictive analytics, learning, real-time monitoring, decision-making, and resource management, thereby benefiting engineers and policymakers, among other stakeholders. Demand forecasting, flood management, and smart water quality monitoring enhance resource management, disaster prevention, and eco-conservation. On numerous occasions, AI models outcompete traditional hydrological approaches in terms of accurate water level and inflow predictions. In addition, the combination of AI with IoT sensors means that potential and actual conditions of dams and water quality are constantly monitored to optimize maintenance programs and avoid incidents. Problems arising from data quality and availability, interpretability of models, and the requirement of being a competent technical person hinder its widespread use. Similarly, ethical and legal considerations such as privacy and responsibility pose challenges to integrating AI into current systems. Addressing these challenges is very important if the impact of AI is to be enhanced. As highlighted in this analysis, there is a need for multi-disciplinary non-kingdom collaboration and specific expenditure to deal with these constraints. Each requires a greater research effort to improve our abilities to advance from non-parametric approaches to new paradigms of predictive modeling, big data, and real-time decision support and to become more responsible stewards of the Earth’s limited water supplies. The outcomes highlight AI’s potential to change water management and improve its effectiveness and sustainability. This research underscores the importance of ensuring a sustainable and secure water supply in the future to enable the provision of water supply as stipulated in the global sustainable environment and structures’ agenda.

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Author Biographies

Layth Abdulameer, Petroleum Engineering Department, University of Kerbala, Karbala 56001, Iraq

laith.saeed@uokerbala.edu.iq

Najah Mahdi Lateef Maimuri, Building and Construction Technologies Engineering Department, College of Engineering and Engineering Technologies, Al-Mustaqbal University, Babylon, Hillah, 51001, Iraq

najahml@yahoo.com

Ala Hassan Nama, Water Resources Department/ College of Engineering/ University of Baghdad, Baghdad, Iraq

ala.hassan@coeng.uobaghdad.edu.iq

Farhan Lafta Rashid, Petroleum Engineering Department, University of Kerbala, Karbala 56001, Iraq

farhan.lefta@uokerbala.edu.iq

Hayder Ibrahim Mohammed, Department of Physics, College of Education, University of Garmian, Kalar 46021, Iraq

hayder.i.moahmmad@garmian.edu.krd

Ahmed Noori Ghani Al-Dujaili, Amirkabir University of Technology/ Petroleum Engineering Department, No. 350, Hafez Ave, Valiasr Square, Tehran, 1591634311 Iran

ahmed.noori203@aut.ac.ir

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Published

2025-03-20

How to Cite

Abdulameer, L., Maimuri, N. M. L., Nama, A. H., Rashid, F. L., Mohammed, H. I., & Al-Dujaili, A. N. G. (2025). Review of Artificial Intelligence Applications in Dams and Water Resources: Current Trends and Future Directions. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 128(2), 205–225. https://doi.org/10.37934/arfmts.128.2.205225

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