IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i20p13384-d944949.html
   My bibliography  Save this article

Smart Water Resource Management Using Artificial Intelligence—A Review

Author

Listed:
  • Siva Rama Krishnan

    (School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India)

  • M. K. Nallakaruppan

    (School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India)

  • Rajeswari Chengoden

    (School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India)

  • Srinivas Koppu

    (School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India)

  • M. Iyapparaja

    (School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India)

  • Jayakumar Sadhasivam

    (School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India)

  • Sankaran Sethuraman

    (Council of Scientific and Industrial Research, CSIR-National Geophysical Research Institute, Hyderabad 500007, India)

Abstract

Water management is one of the crucial topics discussed in most of the international forums. Water harvesting and recycling are the major requirements to meet the global upcoming demand of the water crisis, which is prevalent. To achieve this, we need more emphasis on water management techniques that are applied across various categories of the applications. Keeping in mind the population density index, there is a dire need to implement intelligent water management mechanisms for effective distribution, conservation and to maintain the water quality standards for various purposes. The prescribed work discusses about few major areas of applications that are required for efficient water management. Those are recent trends in wastewater recycle, water distribution, rainwater harvesting and irrigation management using various Artificial Intelligence (AI) models. The data acquired for these applications are purely unique and also differs by type. Hence, there is a dire need to use a model or algorithm that can be applied to provide solutions across all these applications. Artificial Intelligence (AI) and Deep Learning (DL) techniques along with the Internet of things (IoT) framework can facilitate in designing a smart water management system for sustainable water usage from natural resources. This work surveys various water management techniques and the use of AI/DL along with the IoT network and case studies, sample statistical analysis to develop an efficient water management framework.

Suggested Citation

  • Siva Rama Krishnan & M. K. Nallakaruppan & Rajeswari Chengoden & Srinivas Koppu & M. Iyapparaja & Jayakumar Sadhasivam & Sankaran Sethuraman, 2022. "Smart Water Resource Management Using Artificial Intelligence—A Review," Sustainability, MDPI, vol. 14(20), pages 1-28, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13384-:d:944949
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/20/13384/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/20/13384/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. López-Riquelme, J.A. & Pavón-Pulido, N. & Navarro-Hellín, H. & Soto-Valles, F. & Torres-Sánchez, R., 2017. "A software architecture based on FIWARE cloud for Precision Agriculture," Agricultural Water Management, Elsevier, vol. 183(C), pages 123-135.
    2. Berthet, Alice & Vincent, Audrey & Fleury, Philippe, 2021. "Water quality issues and agriculture: An international review of innovative policy schemes," Land Use Policy, Elsevier, vol. 109(C).
    3. Peace Bamurigire & Anthony Vodacek & Andras Valko & Said Rutabayiro Ngoga, 2020. "Simulation of Internet of Things Water Management for Efficient Rice Irrigation in Rwanda," Agriculture, MDPI, vol. 10(10), pages 1-12, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yi Wang & Yuhan Cheng & He Liu & Qing Guo & Chuanjun Dai & Min Zhao & Dezhao Liu, 2023. "A Review on Applications of Artificial Intelligence in Wastewater Treatment," Sustainability, MDPI, vol. 15(18), pages 1-28, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kushan D. Siriwardhana & Dimantha I. Jayaneththi & Ruchiru D. Herath & Randika K. Makumbura & Hemantha Jayasinghe & Miyuru B. Gunathilake & Hazi Md. Azamathulla & Kiran Tota-Maharaj & Upaka Rathnayake, 2023. "A Simplified Equation for Calculating the Water Quality Index (WQI), Kalu River, Sri Lanka," Sustainability, MDPI, vol. 15(15), pages 1-15, August.
    2. Mabhaudhi, Tafadzwanashe & Dirwai, Tinashe Lindel & Taguta, Cuthbert & Sikka, Alok & Lautze, Jonathan, 2023. "Mapping Decision Support Tools (DSTs) on agricultural water productivity: A global systematic scoping review," Agricultural Water Management, Elsevier, vol. 290(C).
    3. Emin Guresci & Bedir Tekinerdogan & Önder Babur & Qingzhi Liu, 2024. "Feasibility of Low-Code Development Platforms in Precision Agriculture: Opportunities, Challenges, and Future Directions," Land, MDPI, vol. 13(11), pages 1-31, October.
    4. Luis Emmi & Roemi Fernández & Pablo Gonzalez-de-Santos & Matteo Francia & Matteo Golfarelli & Giuliano Vitali & Hendrik Sandmann & Michael Hustedt & Merve Wollweber, 2023. "Exploiting the Internet Resources for Autonomous Robots in Agriculture," Agriculture, MDPI, vol. 13(5), pages 1-22, May.
    5. Safa Baccour & Gerwin Goelema & Taher Kahil & Jose Albiac & Michelle T. H. Vliet & Xueqin Zhu & Maryna Strokal, 2024. "Water quality management could halve future water scarcity cost-effectively in the Pearl River Basin," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    6. Jiahong Yuan & Xiaoyu Li & Zilai Sun & Junhu Ruan, 2021. "Will the Adoption of Early Fertigation Techniques Hinder Famers’ Technology Renewal? Evidence from Fresh Growers in Shaanxi, China," Agriculture, MDPI, vol. 11(10), pages 1-17, September.
    7. Gaetano Rocco & Claudia Pipino & Claudio Pagano, 2023. "An Overview of Urban Mobility: Revolutionizing with Innovative Smart Parking Systems," Sustainability, MDPI, vol. 15(17), pages 1-17, September.
    8. Kelemen, Eszter & Megyesi, Boldizsár & Matzdorf, Bettina & Andersen, Erling & van Bussel, Lenny G.J. & Dumortier, Myriam & Dutilly, Céline & García-Llorente, Marina & Hamon, Christine & LePage, Annabe, 2023. "The prospects of innovative agri-environmental contracts in the European policy context: Results from a Delphi study," Land Use Policy, Elsevier, vol. 131(C).
    9. Galvin, Emily M. & BenDor, Todd K., 2023. "The economic impacts of green stormwater infrastructure: An evaluation of novel stormwater management policies in Washington, D.C," Land Use Policy, Elsevier, vol. 134(C).
    10. Zinkernagel, Jana & Maestre-Valero, Jose. F. & Seresti, Sogol Y. & Intrigliolo, Diego S., 2020. "New technologies and practical approaches to improve irrigation management of open field vegetable crops," Agricultural Water Management, Elsevier, vol. 242(C).
    11. Krunal K. Punjani & Kala Mahadevan & Angappa Gunasekaran & V. V. Ravi Kumar & Sujata Joshi, 2023. "Cloud computing in agriculture: a bibliometric and network visualization analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3849-3883, August.
    12. Ruth Cordova-Cardenas & Luis Emmi & Pablo Gonzalez-de-Santos, 2023. "Enabling Autonomous Navigation on the Farm: A Mission Planner for Agricultural Tasks," Agriculture, MDPI, vol. 13(12), pages 1-19, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13384-:d:944949. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.