Adaptive AI for Smart Water Networks

Welcome to my research blog

Vision

Water is life. Yet in many regions, aging infrastructure, climate change, and unpredictable consumer behavior challenge our ability to deliver it sustainably.

My mission is to develop adaptive AI systems that empower utilities, researchers, and communities to:

• detect leaks early,
• optimize hydraulic networks,
• model pollution and chlorination dynamics,
• and adapt in real time to behavioral disturbances.

Research Topics

1. Hybrid Graph Neural Networks (GNN) + Physics-Informed Neural Networks (PINN) for real-time simulation and anomaly detection in water grids
2. Fractal geometry & metaheuristics to optimize valve placement and zoning (DMA)
3. SCADA blackout simulation using I-PINN + synthetic data
4. Water quality modeling including disinfectant decay and pollutant tracking
5. Multi-agent systems with RL for tariff-policy simulation and response
6. GeoAI and vector databases for smart urban water analytics (Neo4j + PostGIS + SCADA + LangChain)

Get Involved

• Explore my GitHub projects
• Read my latest publications
• Watch my presentation at Global Water Summit 2025
• Follow me on LinkedIn
• Request a feature or collaboration

Tools & Methods

• Python stack: PyTorch Geometric, DeepXDE, pymoo, Streamlit, WNTR, epanet-python
• Databases: Neo4j, PostGIS, TimescaleDB, Vector DBs for AI
• Frameworks: Hugo Blox + GitHub Pages
• Deployment: Docker, API-driven services for interactive dashboards

Themes & Publishing

This site is powered by Hugo Blox, with fully customizable design and automated publishing. All content is written in Markdown + YAML for transparency and simplicity.

Credits

• This site template is based on Hugo Blox
• © 2025 Tetiana Starovoit. All rights reserved.
• Released under the MIT License.