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

In recent decades, many AI techniques (knowledge-based systems, neural networks, evolutionary systems, fuzzy logic, genetic algorithms, adaptive agents, expert systems) have been developed in water engineering. Effective water use depends on intelligent management of both supply and demand, supported by smart water (SW) technologies for operational monitoring, evaluation, projections, and forecasting. The paper’s objectives are to shed light on applied intelligence across water-related SDGs, explain why applied intelligence succeeds where optimization may fail, and suggest using these techniques when optimal or exact solutions are too costly or infeasible. The study explores how AI can enhance the wise use of freshwater across development actions tied to global goals, focusing on conceptual work with broad interdisciplinary scope given water’s centrality. Research questions: 1. How can intelligent water applications be implemented, and what are their components? 2. What are the features of intelligent water systems (IWSs), their contributions, and future opportunities that could be provided by intelligent water hackathons in the Arabsphere?

Coronaviruses (COVID-19) pandemic: The pandemic created crises in food, health, and water security, affecting the Water-Climate-Food Nexus in Arab societies. While lockdowns briefly improved air and water quality, risks to sanitation, hygiene, and food safety may threaten supplies (FAO 2020). Numerous studies modeled COVID-19 spread and impacts using machine learning (RF, SVM, XGBoost, K-means, neural networks, logistic regression, MLP, ANFIS) and deep learning (CNN variants, GANs, 3D DCNN, residual networks, autoencoders), including forecasting cases/deaths and clustering spatial spread. These works illustrate the maturity and breadth of intelligent computing for complex, uncertain systems, relevant to water domain forecasting and decision support. Water stress (WS) indicator: UN SDG 6 targets sustainable water for all; indicator 6.4.2 measures level of water stress as TFWW/(TRWR − EFR) × 100, reflecting pressure on water resources and sustainability of use. Thresholds: <25% (no stress), 25–50% (low), 50–75% (medium), 75–100% (high), >100% (critical).

Study scope—Arabsphere: The Arabsphere includes 22 countries where sustainable development is tightly coupled to the Water–Food–Climate nexus. Vulnerabilities arise from climate variability and extremes, with strategic emphasis on unconventional water (desalination, drainage water reuse, treated grey/wastewater, brackish groundwater, rain/dew/fog harvesting). Computation of water stress (WS): Using FAO AQUASTAT data for SDG 6.4.2 (year 2020), water stress is defined as: WS (%) = TFWW/(TRWR − EFR) × 100, with TFWW, TRWR, and EFR in 10^6 m³/year. Classes apply thresholds at 25%, 50%, and 75%. Data validation follows FAO procedures: cross-variable comparison, time-series coherency, metadata verification, statistical validation rules, and imputation (linear, carry-forward, vertical) where needed. Smart Water Systems (SWSs) data and architecture: SWSs require multi-source data—meteorological (precipitation, evaporation, temperature, wind), hydrologic (gauges, discharge, reservoir levels, water quality), and remote sensing (precipitation, vegetation, evapotranspiration, soil moisture, flood extent). An integrative smart water knowledge system aggregates existing data, information, and science across assets; provides real-time, on-demand data; integrates with heuristic knowledge bases; and supports alerting on anomalies/incidents. Robust knowledge acquisition includes synchronization across space and time, establishing variability and confidence intervals, and enabling analysis, design, and interpretation via graphical interfaces and application software. Identification and interpretation processes: Reliable observations and measurements underpin system identification of complex, dynamic, non-stationary water systems. Data must be synchronized and quality-assured before processing, archiving, and interpretation. Intelligent water hackathon process and design: The Intelligent Water Hackathon is a collaborative open-science event designed to mitigate WS in the Arabsphere. It comprises four phases: problem identification, team building, solution proposing, and presentation. Multidisciplinary participation spans government, academia, industry, rural/urban communities, and stakeholders, including experts (engineers, programmers, administrators, public health specialists, environmentalists) and non-experts (consumers/users). The process promotes community engagement and balanced teams, with analytical, experimental, knowledge technology, and decision-making paths, and a before/during/after workflow for implementation and prototyping (per the smart water hackathon flow).

- The Arabsphere faces critical overall water stress: aggregate WS = 111.39% (critical) based on FAO/UN-Water 2021 data for 2020, with TRWR = 288.91×10^9 m³/yr, TFWW = 255.66×10^9 m³/yr, EFR = 59.4×10^9 m³/yr. - Country-level WS highlights (2020): • Extremely critical: Kuwait 3850.5%, UAE 1630.67%, Saudi Arabia 974.17%, Libya 817.14%, Qatar 431.03%, Yemen 169.76%, Algeria 137.92%, Egypt 141.17%, Oman 116.71%, Jordan 104.31%, Sudan 118.66%, Syria 124.36% (all critical >100%). • High (75–100%): Iraq 79.51%, Tunisia 96%. • Medium (50–75%): Lebanon 58.79%, Morocco 50.75%. • Low/No stress: State of Palestine 47.01% (low), Somalia 24.53%, Mauritania 13.25%, Djibouti 6.33%, Comoros 0.83% (no stress). - Regional comparison (2020): Northern Africa and Western Asia WS = 84.07% (high) vs World WS = 18.55% (no stress). Other regions largely show no stress or low/medium stress, underscoring the Arabsphere’s severity. - The study synthesizes significant applied intelligence techniques for SWSs (neural networks, evolutionary computing, fuzzy logic, rough sets) and application areas (time-series prediction, real-time monitoring, anomaly/fault detection, planning/scheduling, dynamic modeling, quality prediction/inspection, leakage detection, telecommunications). - Smart water system success factors include stakeholder engagement, data accessibility, adequate monitoring networks, predictive capabilities, conflict resolution mechanisms, and demonstrable improvements in behavior/actions. - AI-enabled opportunities for a sustainable water future (SWF) span agriculture, domestic, and industrial sectors: governance and planning, forecasting river/groundwater flows, disaster risk management, water quality assurance, contaminant transport, smart metering and leakage detection, climate/weather forecasting, climate-smart agriculture, optimal land use, and circular water economy. - Hackathons are identified as valuable mechanisms to generate creative ideas, collective knowledge, strengthen community engagement, and catalyze solutions that can mitigate water stress across the Arabsphere.

The analysis confirms that the Arabsphere’s water stress is critical in aggregate and, for many countries, exceeds 100%, indicating withdrawals surpass available renewable resources after environmental flow requirements. This addresses the research focus by highlighting where intelligent water applications are most urgently needed and by delineating system components and data pipelines for SWS deployment. AI and smart water approaches broaden the solution space beyond exact optimization, enabling tractable, robust solutions under uncertainty. When integrated into knowledge systems with real-time monitoring and predictive modeling, AI can improve allocation, operations, and crisis response. Intelligent water hackathons serve as a bridging mechanism among stakeholders and disciplines to co-create practical, explainable AI solutions tailored to local contexts, thereby advancing SDG 6.4 (reducing water scarcity) and contributing to wider SDGs. The significance to the field lies in framing AI-driven water management as an integrative socio-technical system—combining data, models, heuristics, and governance—while demonstrating how community-driven innovation (hackathons) can accelerate adoption and address pressing WS challenges in the Arabsphere.

Artificial and applied intelligence in hydrology and water resources constitutes a promising field for practical problem-solving under uncertainty. Smart Water Systems (SWSs) integrate current observations with forecasts to guide managers in optimizing use, mitigating droughts/floods, and protecting environmental quality. The approach narrows gaps between modeling and practice by encoding decision processes and enhancing model usability. For the Arabsphere, where aggregate water stress is critical, AI-driven strategies and intelligent water hackathons can catalyze creative, community-informed solutions that target leakage reduction, efficient allocation, quality monitoring, and resilient planning. Advancing a sustainable water future requires adequate resources, integrated multidisciplinary teams, and partnerships across academia, government, and industry. AI-informed policies can also support recovery from COVID-19 while fostering livable societies. The work encourages prioritizing an Arab water vision centered on AI and IWSs to mitigate water stress and scarcity risks for future generations.

The study is primarily conceptual and synthesizes existing datasets (FAO AQUASTAT) and literature rather than conducting new empirical trials. WS indicators rely on 2020 data and standardized validation processes, which may mask local data gaps or uncertainties. Adoption of AI poses challenges including opacity/incomprehensibility, governance and ethical concerns, and the need for explainable, responsible AI. Effective deployment requires substantial resources, robust monitoring networks, data integration capacity, stakeholder engagement, and cross-sector coordination, which may limit generalizability and near-term implementation in some contexts.