Could ChatGPT Automate Water Network Clustering? A Performance Assessment Across Algorithms

Water distribution networks (WDNs) are characterized by complex challenges in management and optimization, especially in ensuring efficiency, reducing losses, and maintaining infrastructure performances. The recent advancements in Artificial Intelligence (AI) techniques based on Large Language Model...

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Vydané v:Water (Basel) Ročník 17; číslo 20; s. 2995
Hlavní autori: Palma, Ludovica, Creaco, Enrico, Iervolino, Michele, Marocco, Davide, Santonastaso, Giovanni Francesco, Di Nardo, Armando
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 17.10.2025
Predmet:
Algorithms
Analysis
Automation
Case studies
Chatbots
Cluster analysis
Clustering
Comparative analysis
Datasets
Distribution
Forecasts and trends
Hydraulics
Machine learning
Market segmentation
Market segments
Optimization
Software
Water
Water quality
Water resources management
Water shortages
Italy
ISSN:2073-4441, 2073-4441
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Shrnutí:Water distribution networks (WDNs) are characterized by complex challenges in management and optimization, especially in ensuring efficiency, reducing losses, and maintaining infrastructure performances. The recent advancements in Artificial Intelligence (AI) techniques based on Large Language Models, particularly ChatGPT 4.0 (a chatbot based on a generative pre-trained model), offer potential solutions to streamline these processes. This study investigates the ability of ChatGPT to perform the clustering phase of WDN partitioning, a critical step for dividing large networks into manageable clusters. Using a real Italian network as a case study, ChatGPT was prompted to apply several clustering algorithms, including k-means, spectral, and hierarchical clustering. The results show that ChatGPT uniquely adds value by automating the entire workflow of WDN clustering—from reading input files and running algorithms to calculating performance indices and generating reports. This makes advanced water network partitioning accessible to users without programming or hydraulic modeling expertise. The study highlights ChatGPT’s role as a complementary tool: it accelerates repetitive tasks, supports decision-making with interpretable outputs, and lowers the entry barrier for utilities and practitioners. These findings demonstrate the practical potential of integrating large language models into water management, where they can democratize specialized methodologies and facilitate wider adoption of WDN managing strategies.
Bibliografia:ObjectType-Article-1
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ISSN:2073-4441
2073-4441
DOI:10.3390/w17202995