GNN | Hugo Academic CV Theme

Research Project Planning & AI Experiments Management

Sat, 14 Jun 2025 00:00:00 +0000

Effectively manage your AI-driven research projects for smart water networks — from ideation to simulation, paper writing, and presentations.

Ideation (Mind Mapping)

Use Hugo Blox’s mindmap extension to brainstorm ideas for a new hybrid AI model or grant proposal (e.g. Horizon Europe or Global Water Summit initiative):

- AquaTwin+ Project
- Hybrid AI
- GNN
- PINN
- Fuzzy Logic
- SCADA Integration
- Pollution Modelling
- Leak Detection
- DMA Zoning
- Partners
- TUHH
- Hamburg Wasser
- КПІ
- Київводоканал
- Siemens
gantt
dateFormat YYYY-MM-DD
title Hybrid AI for Smart Water Networks
section Setup
Define Objectives :done, 2025-05-01, 5d
Prepare Data :done, 2025-05-06, 5d
section Development
GNN Modeling :active, 2025-05-11, 10d
PINN Integration :2025-05-21, 7d
Pollution Scenarios :2025-05-28, 5d
section Dissemination
Prepare GitHub Repo :2025-06-02, 2d
Global Summit Slides :2025-06-04, 2d
- [x] Import Walkerton EPANET model
- [x] Add base demand & chlorine decay config
- [x] Integrate PINN for unknown pipe zones
- [x] Train GNN on simulated leaks
- [ ] Implement RL agent for tariff control
- [ ] Deploy interactive dashboard (Streamlit/Leaflet)
- [ ] Prepare Horizon Europe proposal PDF

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
Released under the MIT License.

Adaptive AI for Water Utilities: From SCADA to Digital Twins

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

Visualizing Smart Water Systems with AI & GIS

Wed, 30 Apr 2025 00:00:00 +0000

In my recent work on hybrid AI models for water infrastructure, I needed ways to effectively explain system behavior, learning outcomes, and detected anomalies. Here are tools I used with HugoBlox to communicate those insights interactively and clearly.

Interactive Charts with Plotly

I used Plotly for dynamic visualizations of:

Predicted vs actual pressure across nodes
Leak probability heatmaps over time
Consumption anomalies from SCADA time series

Save a Plotly .json file (e.g., leak-probability.json) in your page folder and display it like this:







graph TD
A[SCADA Data] --> B(GNN Inference)
B --> C{Leak Detected?}
C -->|Yes| D[Trigger Alert]
C -->|No| E[Continue Monitoring]


sequenceDiagram
SCADA->>GNN: Feed pressure data
GNN->>PINN: Pass edge/node outputs
PINN-->>SCADA: Simulated parameters
Note right of PINN: Feedback to SCADA control