Artificial Intelligence for Water Consumption Assessment: State of the Art Review
Almando Morain,
Nivedita Ilangovan,
Christopher Delhom and
Aavudai Anandhi (anandhi@famu.edu)
Additional contact information
Almando Morain: Florida Agricultural and Mechanical University, FSH Science Research Center
Nivedita Ilangovan: Woodbridge Academy Magnet School
Christopher Delhom: USDA-ARS Stoneville
Aavudai Anandhi: Florida Agricultural and Mechanical University
Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 9, No 2, 3113-3134
Abstract:
Abstract In recent decades, demand for freshwater resources has increased the risk of severe water stress. With the growing prevalence of artificial intelligence (AI), many researchers have turned to it as an alternative to linear methods to assess water consumption (WC). Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, this study utilized 229 screened publications identified through database searches and snowball sampling. This study introduces novel aspects of AI's role in water consumption assessment by focusing on innovation, application sectors, sustainability, and machine learning applications. It also categorizes existing models, such as standalone and hybrid, based on input, output variables, and time horizons. Additionally, it classifies learnable parameters and performance indexes while discussing AI models' advantages, disadvantages, and challenges. The study translates this information into a guide for selecting AI models for WC assessment. As no one-size-fits-all AI model exists, this study suggests utilizing hybrid AI models as alternatives. These models offer flexibility regarding efficiency, accuracy, interpretability, adaptability, and data requirements. They can address the limitations of individual models, leverage the strengths of different approaches, and provide a better understanding of the relationships between variables. Several knowledge gaps were identified, resulting in suggestions for future research.
Keywords: PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework; Machine learning applications; Advantages; disadvantages; and challenges of artificial intelligence for water consumption; Artificial intelligence; Hybrid models; Performance indexes; Learnable parameters; Smart water management (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:38:y:2024:i:9:d:10.1007_s11269-024-03823-x
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DOI: 10.1007/s11269-024-03823-x
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