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Flowing toward peace: How AI and ML models can transform water security and diplomacy

About the blog

Marcello Michael Serrao
Engineer (PhD) Water Treatment and Data at Suez International (France), my passion lies in developing Smart Tools for Urban Water Management.
  • Flowing toward peace: How AI and ML models can transform water security and diplomacy

At the start of 2025, many people around the globe continue to face concerns about national security and personal safety. Amid these challenges, it is essential to highlight the positive impact that Artificial Intelligence (AI) and Machine Learning (ML) can have on society, particularly in the domain of water management in conflict zones. Water security is emerging as a critical global issue, intersecting with national and individual security in profound ways. AI and ML are proving to be transformative in optimizing resource allocation, enhancing resilience, and mitigating threats to water systems.

The complex interplay between water security and geopolitics

Water systems often cross administrative and national boundaries, where upriver states hold a strategic advantage by controlling its flow, necessitating collaborative management and robust international agreements. Cooperation on transboundary water management is essential for the sustainable use of water resources and for achieving Sustainable Development Goal (SDG) 6. Globally, 153 countries share rivers, lakes, and aquifers. As reported by UN Water, transboundary basins span over half of the Earth's land area, represent around 60% of the global freshwater flow, and support more than 40% of the world's population.

Achieving equitable resource management can be challenging. Unfortunately, water has historically been a source of geopolitical tension, as seen in disputes over transboundary rivers and basins such as the Nile River (shared between Ethiopia, Sudan, and Egypt) and the Indus River (between India and Pakistan). As climate change disrupts ecological patterns and exacerbates water variability, and population growth increases demand, these disputes risk becoming more acute.

Three key international water conflicts to keep an eye on in 2025 include the Brahmaputra River (China and India), the Grand Ethiopian Renaissance Dam on the Nile (Ethiopia, Egypt, and Sudan), and the Ilisu Dam on the Tigris River (Turkey and Iraq). In each case, competition over limited water resources heightens the risk of future inter-state conflict.

Leveraging AI/ML tools for water security

AI and ML offer immense potential in addressing the water security and geopolitical challenges of transboundary basins. These technologies support data-driven decision-making, foster collaboration, and enhance predictive capabilities. What follows is a list of key applications :

1. Modeling of Water Systems and Forecasting

  • AI and ML models can improve the accuracy of hydrological forecasts, including river flow, rainfall, and drought patterns. These forecasts enable optimized reservoir management, efficient resource allocation, and preparation for extreme events.
  • Predictive models can analyze satellite data, historical flow and rainfall records, and real-time sensor inputs of flowrates to guide reservoir operations, minimizing downstream impacts.
  • AI-powered hybrid models reduce model error and can advance automated process control in water distribution networks and wastewater treatment systems, to assure overall performance and compliance (read also my blog on hybrid models in the water industry), thereby reducing the risk of conflict over polluted waters.
  • AI and ML can improve irrigation efficiency for agricultural usage (which consumes a significant portion of water available) by offering real-time recommendations based on weather, soil moisture, and crop needs. Additionally, AI-tools can assist in reducing water wastage in agriculture which then alleviates pressure on shared water resources, contributing to regional stability.

    2. Scenario Analysis and Negotiation Support

    • AI tools can simulate scenarios for water allocation and resource management, considering factors like climate variability, population growth, and economic development. These simulations help stakeholders understand trade-offs and build consensus on equitable solutions.
    • Decision-support systems powered by AI provide policy recommendations that balance the needs of all parties, fostering trust and cooperation.

      3. Crisis Management and Early Warning Systems

      • ML models enhance early warning systems for floods and droughts, reducing vulnerability to climate change. By integrating data from multiple sources, such systems offer timely alerts to communities and policymakers. These capabilities mitigate the humanitarian and economic impacts of extreme events, reducing the likelihood of conflict over water resources.

      4. Protecting Water Infrastructure from Threats

      • Cybersecurity: AI can detect and neutralize cyber threats in real time, addressing vulnerabilities in SCADA systems and IoT devices. See also my blog on AI-tools for cyber security.
      • Physical Security: AI-powered drones and surveillance systems using image-recognition enable real-time monitoring of critical infrastructure, identifying entry of unauthorized personnel and other potential threats early-on.

      5. Transparency and Data Sharing

      • AI-driven platforms facilitate data sharing among governmental bodies and transboundary basin countries, ensuring reliable and consistent information about water flows and operations. Blockchain technology, combined with AI, ensures the integrity and transparency of shared data.
      • Greater transparency builds trust among stakeholders and reduces misinformation, often a source of exacerbated tensions within transboundary cooperation.

      AI-Driven support in conflict and crisis management

      Water diplomacy stands to benefit significantly from AI tools, which can enhance negotiations by analyzing complex hydrological, geopolitical, and socio-economic data. These tools provide deeper insights that can drive more effective transboundary water-sharing agreements and improved policy outcomes. Specific applications include:

        • Predicting water stress in conflict-prone regions, aiding military and humanitarian organizations in their planning and response efforts.
        • Optimizing resource distribution during crises to ensure equitable access to water and mitigate social unrest.
        • Enhancing negotiation outcomes through comprehensive data analytics, allowing stakeholders to make more informed decisions based on real-time data.
        1. In addition to these applications, AI and Machine Learning can play a critical role in training of water operators how to handle crisis situations, particularly when combined with Augmented Reality (AR) and Virtual Reality (VR) technologies. By immersing water management personnel in simulated crisis scenarios, AR and VR offer hands-on, risk-free environments for training operators to handle complex water management systems under pressure. AI and ML algorithms can further refine these simulations, adapting them to individual training needs and learning progress. This approach not only accelerates the development of operator skills but also ensures that they are well-prepared to manage water systems effectively during real-world conflicts and crises.

        Challenges and ethical considerations

        While AI and ML offer great promise in water conflict zones, their deployment faces several significant challenges, particularly around data accessibility and trust-building. Some thoughts to consider about:

        • Data Availability and Quality: Reliable, high-quality data on hydrology, climate, and socio-economic factors are crucial for effective decision-making. However, such data is often scarce, inconsistent, or difficult to obtain in many conflict-affected regions.
        • Political Will and Trust: Successful AI-driven solutions require cooperation and trust among various stakeholders. In geopolitically sensitive environments, fostering this collaboration can be a considerable challenge.
        • Equity and Inclusion: For AI solutions to lead to sustainable outcomes, they must be accessible and beneficial to all communities, including marginalized or vulnerable groups. Ensuring equity and inclusivity in the application of these technologies is essential for long-term success.

        Conclusion

        AI and ML tools have the potential to revolutionize water security and address the geopolitical challenges of regions with water conflicts and insecurity. By enabling data-driven decision-making, fostering transparency, and enhancing resilience to climate change, these technologies can pave the way for equitable and sustainable water management. However, success depends on addressing data and trust issues and ensuring technological interventions are inclusive and collaborative.

        As tensions simmer globally, leveraging AI and ML applications offers a path toward a more cooperative and secure future. Transboundary collaboration on water management among governments, NGOs, and private sectors will be vital to harness their potential responsibly. The future of water resilience and security depends on innovation, ethics, and cooperation.

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