The Elusive Nature of Roughness: Linking Hydraulics and Graph Theory for Water Distribution Networks Model Calibration
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866908991384190976 |
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| author | Dykiert, Karol Stolarski, Mateusz Czuba, Michał Cieżak, Wojciech Bródka, Piotr |
| author_facet | Dykiert, Karol Stolarski, Mateusz Czuba, Michał Cieżak, Wojciech Bródka, Piotr |
| contents | Accurate pipe roughness estimation in large-scale water distribution networks is often hindered by the high cost of traditional field methods. This study investigates whether network partitioning, by utilizing hydraulic and graph-derived attributes, can enhance the calibration of these parameters. Using a high-fidelity model of a real network as a benchmark, we evaluate density-based clustering, and topology-driven grouping strategies. Optimization experiments demonstrate that attribute-based grouping yields stable, repeatable results comparable to manual calibration for hydraulically significant pipes. While hydraulic attributes generate more distinct cluster structures, the inclusion of graph-based data improves calibration robustness by stabilizing the optimization process. Notably, density-based clustering achieves similar accuracy to k-means while reducing computational effort in specific configurations. Although the method does not eliminate all sources of uncertainty, results suggest that topology-informed grouping provides a systematic, reproducible, and computationally efficient alternative to manual heuristics, highlighting the critical role of network structure in reliable parameter estimation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22809 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Elusive Nature of Roughness: Linking Hydraulics and Graph Theory for Water Distribution Networks Model Calibration Dykiert, Karol Stolarski, Mateusz Czuba, Michał Cieżak, Wojciech Bródka, Piotr Computational Engineering, Finance, and Science Social and Information Networks Systems and Control Accurate pipe roughness estimation in large-scale water distribution networks is often hindered by the high cost of traditional field methods. This study investigates whether network partitioning, by utilizing hydraulic and graph-derived attributes, can enhance the calibration of these parameters. Using a high-fidelity model of a real network as a benchmark, we evaluate density-based clustering, and topology-driven grouping strategies. Optimization experiments demonstrate that attribute-based grouping yields stable, repeatable results comparable to manual calibration for hydraulically significant pipes. While hydraulic attributes generate more distinct cluster structures, the inclusion of graph-based data improves calibration robustness by stabilizing the optimization process. Notably, density-based clustering achieves similar accuracy to k-means while reducing computational effort in specific configurations. Although the method does not eliminate all sources of uncertainty, results suggest that topology-informed grouping provides a systematic, reproducible, and computationally efficient alternative to manual heuristics, highlighting the critical role of network structure in reliable parameter estimation. |
| title | The Elusive Nature of Roughness: Linking Hydraulics and Graph Theory for Water Distribution Networks Model Calibration |
| topic | Computational Engineering, Finance, and Science Social and Information Networks Systems and Control |
| url | https://arxiv.org/abs/2604.22809 |