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Artificial Intelligence for Water Consumption Assessment: State of the Art Review

Author

Listed:
  • 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)

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.

Suggested Citation

  • Almando Morain & Nivedita Ilangovan & Christopher Delhom & Aavudai Anandhi, 2024. "Artificial Intelligence for Water Consumption Assessment: State of the Art Review," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(9), pages 3113-3134, July.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:9:d:10.1007_s11269-024-03823-x
    DOI: 10.1007/s11269-024-03823-x
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    1. Guoqiang Chen & Tianyu Long & Jiangong Xiong & Yun Bai, 2017. "Multiple Random Forests Modelling for Urban Water Consumption Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4715-4729, December.
    2. Marcos Geraldo Gomes & Victor Hugo Carlquist da Silva & Luiz Fernando Rodrigues Pinto & Plinio Centoamore & Salvatore Digiesi & Francesco Facchini & Geraldo Cardoso de Oliveira Neto, 2020. "Economic, Environmental and Social Gains of the Implementation of Artificial Intelligence at Dam Operations toward Industry 4.0 Principles," Sustainability, MDPI, vol. 12(9), pages 1-19, April.
    3. Maltais, Louis-Gabriel & Gosselin, Louis, 2021. "Predictability analysis of domestic hot water consumption with neural networks: From single units to large residential buildings," Energy, Elsevier, vol. 229(C).
    4. E. Pacchin & F. Gagliardi & S. Alvisi & M. Franchini, 2019. "A Comparison of Short-Term Water Demand Forecasting Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1481-1497, March.
    5. Abdüsselam Altunkaynak & Mehmet Özger & Mehmet Çakmakci, 2005. "Water Consumption Prediction of Istanbul City by Using Fuzzy Logic Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(5), pages 641-654, October.
    6. Ashu Jain & Ashish Kumar Varshney & Umesh Chandra Joshi, 2001. "Short-Term Water Demand Forecast Modelling at IIT Kanpur Using Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 15(5), pages 299-321, October.
    7. Yang, Linshan & Feng, Qi & Lu, Tiaoxue & Adamowski, Jan F. & Yin, Zhenliang & Hatami, Shadi & Zhu, Meng & Wen, Xiaohu, 2023. "The response of agroecosystem water use efficiency to cropland change in northwest China’s Hexi Corridor," Agricultural Water Management, Elsevier, vol. 276(C).
    8. Julia K. Ambrosio & Bruno M. Brentan & Manuel Herrera & Edevar Luvizotto & Lubienska Ribeiro & Joaquín Izquierdo, 2019. "Committee Machines for Hourly Water Demand Forecasting in Water Supply Systems," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-11, January.
    9. Alexandra E. Ioannou & Enrico F. Creaco & Chrysi S. Laspidou, 2021. "Exploring the Effectiveness of Clustering Algorithms for Capturing Water Consumption Behavior at Household Level," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
    10. Konstantinos Glynis & Zoran Kapelan & Martijn Bakker & Riccardo Taormina, 2023. "Leveraging Transfer Learning in LSTM Neural Networks for Data-Efficient Burst Detection in Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(15), pages 5953-5972, December.
    11. Xu Gao & Wenru Zeng & Yu Shen & Zhiwei Guo & Jinhui Yang & Xuhong Cheng & Qiaozhi Hua & Keping Yu, 2020. "Integrated Deep Neural Networks-Based Complex System for Urban Water Management," Complexity, Hindawi, vol. 2020, pages 1-12, November.
    12. Mahmut Firat & Mehmet Yurdusev & Mustafa Turan, 2009. "Evaluation of Artificial Neural Network Techniques for Municipal Water Consumption Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(4), pages 617-632, March.
    13. Oluwaseun Oyebode & Desmond Eseoghene Ighravwe, 2019. "Urban Water Demand Forecasting: A Comparative Evaluation of Conventional and Soft Computing Techniques," Resources, MDPI, vol. 8(3), pages 1-18, September.
    14. Mukand Babel & Victor Shinde, 2011. "Identifying Prominent Explanatory Variables for Water Demand Prediction Using Artificial Neural Networks: A Case Study of Bangkok," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(6), pages 1653-1676, April.
    15. Rafael Benítez & Carmen Ortiz-Caraballo & Juan Carlos Preciado & José M. Conejero & Fernando Sánchez Figueroa & Alvaro Rubio-Largo, 2019. "A Short-Term Data Based Water Consumption Prediction Approach," Energies, MDPI, vol. 12(12), pages 1-24, June.
    16. Haidong Huang & Zhixiong Zhang & Fengxuan Song, 2021. "An Ensemble-Learning-Based Method for Short-Term Water Demand Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1757-1773, April.
    17. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
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