Water Infrastructure | Hugo Academic CV Theme

Adaptive AI for Smart Water Networks

Wed, 14 May 2025 00:00:00 +0000

Welcome to my research blog

Table of Contents

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

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

Get Involved

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.

Adaptive AI for Water Utilities: From SCADA to Digital Twins

Mon, 12 May 2025 11:30:00 +0000

Hybrid AI Models for Adaptive Water Infrastructure: GNN and PINN Integration with SCADA Signals

Tue, 10 Dec 2024 00:00:00 +0000

Click the Cite button above to export the BibTeX and add this to your reference manager or Zotero.

These slides provide an overview of the SCADA-integrated PINN-GNN architecture for water supply optimization.

The proposed approach demonstrates how hybrid neural architectures can be applied to real-world water infrastructure challenges. By combining graph topologies with physical modeling constraints and real-time sensor feedback, the framework bridges data-driven and physics-based modeling.

It supports operational decisions such as:

Dynamic leak localization
Adaptive pressure control
Placement of smart valves and sensors
Predictive failure analysis

Future work includes deployment of the system in collaboration with water utilities across Europe within the Horizon Europe framework.