The Arabsphere faces critical water stress, exceeding 100% according to the FAO's water stress indicator. This necessitates innovative solutions to reconcile limited resources with increasing demands. This paper investigates the role of artificial intelligence (AI) in rethinking human-water interactions, reforming water practices, and improving daily water life. The study aims to explore how AI can enhance the wise use of freshwater within the context of the Sustainable Development Goals (SDGs), specifically SDG 6 (clean water and sanitation). The research focuses on the conceptual implementation of intelligent water applications and the potential of intelligent water hackathons in the Arabsphere to foster innovation and community engagement in addressing water stress. The research questions center around the implementation of intelligent water applications and their components, and the features, contributions, and future opportunities offered by intelligent water hackathons in the Arabsphere.

The paper reviews the use of AI techniques in water engineering, including knowledge-based systems, neural networks, and fuzzy logic. It also examines the impact of the COVID-19 pandemic on water security and the food-water-climate nexus in the Arabsphere, noting that while the pandemic led to improvements in air and water quality in some areas due to economic contraction, it also created food and water insecurity challenges. The literature review analyzes various AI-based forecasting models used for COVID-19 and discusses the application of AI in water stress indicators, highlighting the FAO's AQUASTAT database and the calculation of water stress percentages. The review also covers previous research on unconventional water resources, like desalination and reclaimed wastewater, and the role of AI in optimizing their utilization.

The study uses a mixed-methods approach. It analyzes existing data on water stress in the Arabsphere, sourced from FAO's AQUASTAT database. The calculation of water stress is based on the formula: Water stress (%) = TFWW/(TRWR - EFR) × 100, where TFWW represents total freshwater withdrawal, TRWR represents total renewable freshwater resources, and EFR represents environmental flow requirements. Data validation follows FAO's guidelines, including cross-variable comparison, time-series coherency, and metadata verification. The methodology also involves a detailed examination of the concept of intelligent water hackathons as a collaborative, open-science event designed to address water stress. The four phases of the hackathon – problem identification, team building, solution proposing, and presentation – are analyzed. The study further explores the application of applied intelligence methodologies, including neural networks, evolutionary computing, fuzzy logic, and rough sets, in smart water systems. The success of implementing smart water systems (SWSs) is evaluated through a series of questions focusing on stakeholder engagement, data accessibility, water-use planning, hydrologic prediction capability, conflict resolution, and overall improvements in actions and behaviors. The methodology also involves the identification of SWF opportunities provided by AI, particularly in agriculture, domestic uses, and industry, encompassing aspects like water governance, water scarcity, water-related disasters, and stakeholder engagement.

The Arabsphere demonstrates a critical level of water stress, significantly higher than global averages. The study highlights the significant potential of AI in addressing this issue. Applied intelligence techniques, such as neural networks, evolutionary computing, fuzzy logic, and rough sets, are identified as crucial tools for effective smart water system development. Intelligent water hackathons are presented as effective platforms for collaborative innovation and community engagement in tackling water-related challenges. The study outlines the characteristics of an intelligent water system (IWS), emphasizing fault tolerance, uncertainty representation, associative memory, fast learning, and suitability for pattern recognition. The research details the components of a successful smart water system, including data acquisition and analysis, nowcasting, and forecasting at various spatial scales. The successful implementation of a smart water hackathon is visualized through a flow diagram outlining the phases before, during, and after the event. Furthermore, the study explores the role of unconventional water resources, such as desalination, reclaimed water, and atmospheric water harvesting, in meeting future water demands. A framework for water intelligent systems for a sustainable water future is proposed, incorporating elements of vision, foresight, global agenda, cooperation, innovation, education, standardization, feasibility, circular water economy, and evaluation. Several tables summarize key findings, including water stress indicators in the Arabsphere and globally, significant AI techniques for SWSs, and SWF opportunities facilitated by AI. The study emphasizes the need for integrated multidisciplinary teams and partnerships to successfully implement smart water solutions and the importance of addressing the challenges of AI adoption, including the need for explainable AI.

The findings underscore the urgency of addressing water stress in the Arabsphere and highlight AI's significant potential for developing sustainable solutions. Intelligent water hackathons offer a practical and effective framework for fostering collaborative innovation and community engagement. The success of these initiatives relies heavily on integrated multidisciplinary teams, adequate resources, and strong partnerships between academia, government, and industry. The discussion emphasizes the need for responsible AI implementation, addressing potential limitations and ethical considerations. The study’s findings contribute to the growing body of knowledge on AI applications in water resource management, offering valuable insights for policymakers, researchers, and practitioners. The proposed framework for water intelligent systems for SWF provides a roadmap for future research and action. The limitations of existing optimization models and the advantages of AI in tackling complex water problems are discussed.

This research demonstrates the critical need for innovative solutions to address the severe water stress faced by the Arabsphere. The study concludes that intelligent water hackathons, combined with the application of advanced AI techniques, offer a promising pathway towards sustainable water futures. Future research should focus on evaluating the long-term effectiveness of these hackathons, refining AI models for specific regional contexts, and addressing the societal implications of AI adoption in water management. The development of robust and adaptable AI systems for monitoring, forecasting, and managing water resources is crucial.

The study primarily focuses on the conceptual framework and the potential of AI and intelligent water hackathons. While the analysis of existing data on water stress provides valuable context, it does not include empirical data from a specific intelligent water hackathon event. The generalizability of the findings may depend on the successful implementation and replication of similar hackathons in diverse contexts within the Arabsphere. Further research is needed to assess the effectiveness and long-term impact of this approach in different regions and countries.