Water Watch AI Plus

• MVP Developed: Web app (Gemini/GCP) tested by 50+ users, with pilots in 5 villages of Kyrgyzstan.
• Simulation Model: Created for water usage and irrigation planning.
• User Engagement: 20+ expert interviews conducted, app presented to state water agencies.
• Scientific Output:
• Research papers published:
• DOI: 10.1051/bioconf/202412001071
• Application submitted for Software Copyright Certificate.
• Practical Impact: Farmers tested water quality analysis, citizens used the app for household water assessment, and agencies evaluated forecasting scenarios.
• Expected outcomes include up to 10–20% water savings in agriculture and utilities, improved trust in water governance, and reduced risks of water-related conflicts.

• Software & Methods: AI/ML models for hydrological forecasting, MBSE (Model-Based Systems Engineering), discrete-event simulation, BI dashboards, cloud-based web app (MVP built on Gemini + GCP, planned migration to Node.js stack).
• Equipment: IoT sensors (water level, chemical composition, RS-485/Modbus devices), UAVs for aerial monitoring, mobile water sampling stations.
• Auxiliary Systems: Cloud infrastructure with API integration, GIS data layers, historical data archives, secure data storage, and visualization dashboards.

The project faced several challenges:

• Data Access: Hydrometric data from state agencies is often restricted or costly; workaround included using satellite and open-source data.
• Technical Complexity: Integration of IoT sensors, UAVs, AI models, and cloud infrastructure required a multidisciplinary team and iterative testing.
• Seasonal Constraints: Field data collection is limited to March–November, demanding careful scheduling.
• Financial Risks: The project depends on blended funding from grants and partnerships, requiring continuous engagement with donors.
• Institutional Barriers: Limited culture of data sharing and collaboration among agencies created delays in agreements.
  Despite these difficulties, agile project management and strong partnerships helped deliver a working MVP and pilot deployment.

The Water Watch AI Plus project addresses the urgent challenge of sustainable water management in the Kyrgyz Republic and Central Asia. Although Kyrgyzstan is relatively well supplied with water resources, usage is inefficient: up to 95% of water goes to irrigation, while shortages and quality issues persist for households and industries. Climate change increases the risks of floods, droughts, and conflicts over water. Existing monitoring systems are fragmented, outdated, and inaccessible to most stakeholders.

Solution: Water Watch AI Plus is a digital platform for water monitoring and forecasting. It integrates data from IoT sensors, UAV monitoring, satellite and hydrological stations into a unified cloud-based system. Using machine learning, simulation models, and digital twins, it forecasts river flow, water quality, and consumption scenarios.

Functional components:

• Water Resource Database: structured storage of hydrological, chemical, and ecological parameters.
• Remote Monitoring Service: real-time data collection via IoT sensors and UAVs.
• Data Analysis & Visualization: BI dashboards and web portal for government agencies, farmers, researchers, and the public.
• Forecasting & Recommendations: AI-driven models for scenario planning (drought, flood, irrigation scheduling).

Implementation Roadmap:

• Phase 1 (2024–2025): Pilot in Chuy region, creation of MVP, deployment of mobile monitoring stations, testing AI/ML models.
• Phase 2 (2025–2027): Scaling to additional regions, integration of more datasets, enhancement of user interfaces.
• Phase 3 (2027–2028): Regional expansion to Central Asia, support for transboundary water cooperation.

Stakeholders: The project is led by Oscar & Sons Group (KIT LLC) with support from the High Technology Park of Kyrgyzstan, in partnership with the Kyrgyz Hydrometeorological Service, Institute of Water Problems, and scientific institutions.

Funding: Mixed sources, including the High Technology Park, international donors (World Bank, GIZ, EBRD), and competitions (e.g., KIWW World Water Challenge).

By delivering timely, transparent, and predictive insights, Water Watch AI Plus strengthens water security, supports sustainable agriculture, reduces risks of disasters, and fosters collaboration across borders.

• Recognition & Awards:
• Finalist – Astana Digital Bridge 2025 (AI Science Section).
• Finalist – HTP x MIT DeepTech Program.
• II Place – Green Spark Contest Uzbekistan 2025.
• Grand Prix – Best Scientific Project, International Conference on Industry 4.0 Technologies.
• Prize – Best Scientific Report, International Conference on Industry 4.0 Technologies.
• I Place – 100 Ideas for Kyrgyzstan 2024 (Gov. of Kyrgyz Republic).
• II Place – Alatoo Hub Battle 2024.
• Finalist – Deep Tech Pioneer 2024 (Hello Tomorrow).
• Finalist – Build with AI Hackathon 2024 (Google).
• Finalist – Youth Cup for Green Solutions 2024 (Aga Khan Fund).
• I Place – Make-A-Thon.

The pilot implementation covers the Chuy region of Kyrgyzstan, a key agricultural and industrial hub with high water demand. The project has already engaged local communities in 5 villages and cooperates with national research institutes and state agencies.
Future scaling is planned in two dimensions:

1. National – expansion to Issyk-Kul, Naryn, Osh, and other regions of Kyrgyzstan.
2. Regional – deployment across Central Asia, where water scarcity and transboundary rivers are a critical challenge (Syr Darya, Amu Darya basins).

The long-term vision is to create a regional digital water management system, supporting international cooperation and contributing to the water security of millions of people in Central Asia.