Bagheri et al. (2025) Artificial intelligence for the water sector/industry

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-11-14
• Authors: Majid Bagheri, Nader Biglarijoo, Amin Shams, Hamidreza Sharifan, Karim Bagheri, Nakisa Farshforoush, Antonio Velazquez, Maziar Moaveni
• DOI: 10.1016/b978-0-443-34019-2.00005-5

Research Groups

• Department of Engineering Technology, Savannah State University, Savannah, GA, United States
• Civil Engineering Faculty, Semnan University, Semnan, Iran
• Environmental Science and Engineering Program, University of Texas at El Paso, El Paso, TX, United States
• Department of Chemistry and Biochemistry, University of Texas at El Paso, El Paso, TX, United States
• Department of Chemical Engineering, Ilam University, Ilam, Iran
• Department of Electrical and Computer Engineering, Tabriz University, Tabriz, Iran

Short Summary

This chapter comprehensively reviews the diverse applications of artificial intelligence (AI) techniques across critical domains of the water sector, emphasizing their potential to enhance efficiency and sustainability, while also addressing associated challenges and future directions.

Objective

• To review and synthesize the multifaceted applications of artificial intelligence techniques and algorithms within critical domains of the water sector, including water treatment, wastewater treatment, water quality monitoring, water resource management, and smart agriculture and irrigation.

Study Configuration

• Spatial Scale: Conceptual/Global (review of applications across various regions/contexts)
• Temporal Scale: Not applicable (review of existing and future applications of AI)

Methodology and Data

• Models used: Not applicable; this is a review chapter discussing the application of various AI models (e.g., deep learning, machine learning) in the water sector.
• Data sources: Literature review (synthesis of existing research and real-world applications).

Main Results

• Artificial intelligence methodologies are essential for addressing contemporary challenges in the water sector, including those exacerbated by climate change.
• AI demonstrates diverse applications across key areas such as water treatment, wastewater treatment, water quality monitoring, water resource management, and smart agriculture and irrigation.
• The chapter summarizes numerous real-world implementations of AI within the water sector.
• It identifies and discusses significant challenges associated with deploying AI technologies in the water sector and outlines future research and development directions.

Contributions

• Provides a comprehensive and structured overview of the current state and future potential of AI integration across various domains of the water sector.
• Synthesizes both general and specific real-world applications of AI, highlighting its transformative impact on water management.
• Identifies key challenges and proposes future directions for research and implementation of AI technologies in the water sector.

Funding

• Not specified in the provided text.

Citation

@article{Bagheri2025Artificial,
  author = {Bagheri, Majid and Biglarijoo, Nader and Shams, Amin and Sharifan, Hamidreza and Bagheri, Karim and Farshforoush, Nakisa and Velazquez, Antonio and Moaveni, Maziar},
  title = {Artificial intelligence for the water sector/industry},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-34019-2.00005-5},
  url = {https://doi.org/10.1016/b978-0-443-34019-2.00005-5}
}