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

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  • 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
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    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).
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