IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v10y2023i1d10.1057_s41599-023-02146-3.html
   My bibliography  Save this article

A lighthouse to future opportunities for sustainable water provided by intelligent water hackathons in the Arabsphere

Author

Listed:
  • Ayman Batisha

    (Environment and Climate Change Research Institute, National Water Research Center
    Ministry of Scientific Research)

Abstract

Complex water-related challenges hunger, poverty, climate change, biodiversity, land-use change, desertification agriculture, industrialization, urbanization, human population, and hygiene, need wise and urgent actions to overcome them. Globally, many drivers such as the U.S.-Chinese competition, the Russo–Ukrainian war, food security, pandemics, and human overpopulation, have water-related impacts. Freshwater is a truly complex interdisciplinary topic that requires innovative intelligent-inclusive ideas to reconcile limited water resources with expanding water demands. The article explores how artificial intelligence (AI) could rethink human-water interactions, remake water practices, humanize water science, and enhance daily water life. The Global Goals could be viewed as an integrated framework of human effort to face pressing today’s issues and to formulate a more sustainable and better world. Goal 6 (SDG 6 “sustaining water”) devoted to sustaining water and related actions for all humans is the skeleton of global goals (GGs). The Arabsphere faces severe water quality, quantity, and practice challenges to ensure the smooth achievement of global goals (GGs). Compared with the whole world and its main regions, the overall water stress indicator in the Arabsphere is greater than 100% (critical). This article explores how applied intelligence could be strengthened to achieve Goal 6, focuses on the “water stress” indicator, and how to ensure a sustainable water future (SWF) in the Arabsphere. The Intelligent Water Hackathon is a collaborative open science event. The hackathon was designed to mitigate water stress (WS) in the Arabsphere. The hackathon process involves four main phases: problem identification, team building, solution proposing, and presentation. The paper concludes hackathons could be a valuable process for the water researchers’ community to generate new and creative ideas and collective knowledge. Hackathon events could mitigate water stress, strengthen community engagement, and improve water resources outcomes. In closing, artificial intelligence (AI) methodologies are efficient providers to mitigate water stress, scarcity, and related risks. A future-driven Arab water vision based on artificial intelligence (AI) and intelligent water systems (IWSs) should be prioritized.

Suggested Citation

  • Ayman Batisha, 2023. "A lighthouse to future opportunities for sustainable water provided by intelligent water hackathons in the Arabsphere," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
  • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02146-3
    DOI: 10.1057/s41599-023-02146-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-023-02146-3
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1057/s41599-023-02146-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Vincent E.A. Post & Jacobus Groen & Henk Kooi & Mark Person & Shemin Ge & W. Mike Edmunds, 2013. "Offshore fresh groundwater reserves as a global phenomenon," Nature, Nature, vol. 504(7478), pages 71-78, December.
    2. Galaz, Victor & Centeno, Miguel A. & Callahan, Peter W. & Causevic, Amar & Patterson, Thayer & Brass, Irina & Baum, Seth & Farber, Darryl & Fischer, Joern & Garcia, David & McPhearson, Timon & Jimenez, 2021. "Artificial intelligence, systemic risks, and sustainability," Technology in Society, Elsevier, vol. 67(C).
    3. Maleki, Mohsen & Mahmoudi, Mohammad Reza & Heydari, Mohammad Hossein & Pho, Kim-Hung, 2020. "Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Di Vaio, Assunta & Hassan, Rohail & Alavoine, Claude, 2022. "Data intelligence and analytics: A bibliometric analysis of human–Artificial intelligence in public sector decision-making effectiveness," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    5. Yenchun Jim Wu & Mark Goh & Yingping Mai, 2023. "Social innovation and higher education: evolution and future promise," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    6. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    7. Gansser, Oliver Alexander & Reich, Christina Stefanie, 2021. "A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application," Technology in Society, Elsevier, vol. 65(C).
    8. Zhang, Weidong & Zuo, Na & He, Wu & Li, Songtao & Yu, Lu, 2021. "Factors influencing the use of artificial intelligence in government: Evidence from China," Technology in Society, Elsevier, vol. 66(C).
    9. Nazareno, Luísa & Schiff, Daniel S., 2021. "The impact of automation and artificial intelligence on worker well-being," Technology in Society, Elsevier, vol. 67(C).
    10. Melin, Patricia & Monica, Julio Cesar & Sanchez, Daniela & Castillo, Oscar, 2020. "Analysis of Spatial Spread Relationships of Coronavirus (COVID-19) Pandemic in the World using Self Organizing Maps," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    Full references (including those not matched with items on IDEAS)

    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. Zahra Dehghan Shabani & Rouhollah Shahnazi, 2020. "Spatial distribution dynamics and prediction of COVID‐19 in Asian countries: spatial Markov chain approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1005-1025, December.
    2. Khaliq, Abdul & Waqas, Ali & Nisar, Qasim Ali & Haider, Shahbaz & Asghar, Zunaina, 2022. "Application of AI and robotics in hospitality sector: A resource gain and resource loss perspective," Technology in Society, Elsevier, vol. 68(C).
    3. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    4. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    5. Wang, Peipei & Zheng, Xinqi & Ai, Gang & Liu, Dongya & Zhu, Bangren, 2020. "Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    6. Tironi, Martín & Rivera Lisboa, Diego Ignacio, 2023. "Artificial intelligence in the new forms of environmental governance in the Chilean State: Towards an eco-algorithmic governance," Technology in Society, Elsevier, vol. 74(C).
    7. Yulan Li & Kun Ma, 2022. "A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting," IJERPH, MDPI, vol. 19(19), pages 1-17, September.
    8. Yunhan Huang & Quanyan Zhu, 2022. "Game-Theoretic Frameworks for Epidemic Spreading and Human Decision-Making: A Review," Dynamic Games and Applications, Springer, vol. 12(1), pages 7-48, March.
    9. Zhang, Weidong & Zuo, Na & He, Wu & Li, Songtao & Yu, Lu, 2021. "Factors influencing the use of artificial intelligence in government: Evidence from China," Technology in Society, Elsevier, vol. 66(C).
    10. Agbodoh-Falschau, Kouassi Raymond & Ravaonorohanta, Bako Harinivo, 2023. "Investigating the influence of governance determinants on reporting cybersecurity incidents to police: Evidence from Canadian organizations’ perspectives," Technology in Society, Elsevier, vol. 74(C).
    11. Jun Xu, 2024. "AI in ESG for Financial Institutions: An Industrial Survey," Papers 2403.05541, arXiv.org.
    12. Hossain, Mokter, 2022. "The Shenzhen ecosystem: What it means for the western world," Technology in Society, Elsevier, vol. 68(C).
    13. Bhardwaj, Rashmi & Bangia, Aashima, 2020. "Data driven estimation of novel COVID-19 transmission risks through hybrid soft-computing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    14. Homero Rodríguez-Insuasti & Néstor Montalván-Burbano & Otto Suárez-Rodríguez & Marcela Yonfá-Medranda & Katherine Parrales-Guerrero, 2022. "Creative Economy: A Worldwide Research in Business, Management and Accounting," Sustainability, MDPI, vol. 14(23), pages 1-27, November.
    15. Kun Zhang & Xing Huo & Kun Shao, 2023. "Temperature Time Series Prediction Model Based on Time Series Decomposition and Bi-LSTM Network," Mathematics, MDPI, vol. 11(9), pages 1-16, April.
    16. Teichmann, Fabian & Boticiu, Sonia & Sergi, Bruno S., 2023. "RegTech – Potential benefits and challenges for businesses," Technology in Society, Elsevier, vol. 72(C).
    17. Yang, Siying & Liu, Fengshuo, 2024. "Impact of industrial intelligence on green total factor productivity: The indispensability of the environmental system," Ecological Economics, Elsevier, vol. 216(C).
    18. Shalini Shekhawat & Akash Saxena & Ramadan A. Zeineldin & Ali Wagdy Mohamed, 2023. "Prediction of Infectious Disease to Reduce the Computation Stress on Medical and Health Care Facilitators," Mathematics, MDPI, vol. 11(2), pages 1-18, January.
    19. Christenko, Aleksandr, 2022. "Automation and occupational mobility: A task and knowledge-based approach," Technology in Society, Elsevier, vol. 70(C).
    20. Adekoya, Oluwasegun B. & Oliyide, Johnson A. & Saleem, Owais & Adeoye, Habeeb A., 2022. "Asymmetric connectedness between Google-based investor attention and the fourth industrial revolution assets: The case of FinTech and Robotics & Artificial intelligence stocks," Technology in Society, Elsevier, vol. 68(C).

    More about this item

    Statistics

    Access and download statistics

    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:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02146-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.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.