SCADA | Hugo Academic CV Theme

Preprint: Fuzzy-GNN and PINN-based Modeling of Water Infrastructure under SCADA Blackout Conditions

Tue, 10 Jun 2025 00:00:00 +0000

This preprint builds upon the results of our previous published work on GIS-ANFIS-based accident prediction in water networks.

Due to the critical nature of infrastructure data and the wartime conditions in Ukraine, full access to source code and datasets is restricted. Only general architecture and anonymized examples are shared publicly.

The proposed model includes:

Fuzzy membership layers on top of graph node attributes
Integration of PINN for physical constraint modeling (e.g., Darcy–Weisbach)
Compensation mechanisms for missing SCADA sensor inputs
NSGA-II multi-objective optimization for valve control

This approach is applicable in utility AI systems that must remain resilient even during cyber-physical or war-induced infrastructure disruptions.

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

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.