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Updated: Sep 16, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Enhancing explainable AI with graph signal processing: Applications in water distribution systems.

Bruno M Brentan1, Andrea Menapace2, Martin Oberascher3

  • 1Hydraulics and Water Resources Department, Universidade Federal de Minas Gerais, Av. Presidente Antonio Carlos, 6947, Belo Horizonte, 31555250, Minas Gerais, Brazil.

Water Research
|July 10, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a transparent Artificial Intelligence (AI) framework for water distribution systems (WDS). It uses Explainable AI (XAI) and graph signal processing to improve understanding and real-time management of WDS operations.

Keywords:
Asset managementDecision makingExplainable AIGraph signal processingUrban water

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Area of Science:

  • Engineering
  • Computer Science
  • Environmental Science

Background:

  • Water distribution systems (WDS) require advanced monitoring and operational efficiency.
  • Artificial Intelligence (AI) offers solutions but often lacks transparency, hindering adoption.
  • Explainable AI (XAI) is crucial for understanding AI decision-making in critical infrastructure.

Purpose of the Study:

  • To develop a novel framework integrating XAI with graph signal processing for interpretable AI in WDS.
  • To enhance the transparency and understanding of AI models applied to hydraulic states in WDS.
  • To provide a scalable and efficient tool for real-time WDS management and resilience.

Main Methods:

  • Modeling multilayer perceptrons as dynamic, weighted, directed graphs.
  • Utilizing eigencentrality as a graph metric to identify key drivers of AI predictions.
  • Validating the framework using a hydraulic state estimation metamodel and real-world WDS benchmarks.

Main Results:

  • The proposed XAI framework significantly enhances the interpretability of AI models for WDS.
  • Eigencentrality effectively identifies critical factors influencing hydraulic state predictions.
  • The framework demonstrates over 70x faster processing times compared to SHAP and IG, enabling real-time applications.

Conclusions:

  • The integration of graph theory and XAI offers a scalable and transparent solution for WDS.
  • The methodology supports sensor prioritization and maintenance for improved system resilience.
  • This approach facilitates sustainable urban water management through enhanced AI interpretability.