From Text to Simulation: Building EPANET Models from Formalized Network Descriptions with a Local Language Model

Hydraulic models of water distribution networks are the basis of a digital twin, but they are still built mostly by hand. HydraKG is a software pipeline that constructs an EPANET model from a network description and checks the result against that description. In the tabular path, a deterministic parser reads GIS-style attribute tables. In the verbal path, a local large language model (LLM) reads a formalized natural-language description. The network is then checked by validators, by the EPANET solver and by a comparison with the description, none of which uses the source model; code repairs every fact with a single correct value, and the LLM corrects the rest in up to three rounds before the final model is simulated. On seven networks with 11 to 785 nodes, the tabular path reproduces the junction pressures of the source models, as represented internally, with a root-mean-square error (RMSE) of at most 2.1×10−4 m, which is the rounding limit. On canonical descriptions, the verbal path gave a model equal in value to the description in all 52 runs on six networks, including a real distribution zone. On the larger networks, the first extraction omitted groups of elements in every run, and correction rounds restored them.

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Publication Details

Journal
Water
Published
2026-10-30
DOI
https://doi.org/10.3390/w18212721
Primary Topic
Water Systems and Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

From Text to Simulation: Building EPANET Models from Formalized Network Descriptions with a Local Language Model

Michał Gorawski
Water
Water Systems and Optimization
article

From Text to Simulation: Building EPANET Models from Formalized Network Descriptions with a Local Language Model

Michał Gorawski
article en

Abstract

Hydraulic models of water distribution networks are the basis of a digital twin, but they are still built mostly by hand. HydraKG is a software pipeline that constructs an EPANET model from a network description and checks the result against that description. In the tabular path, a deterministic parser reads GIS-style attribute tables. In the verbal path, a local large language model (LLM) reads a formalized natural-language description. The network is then checked by validators, by the EPANET solver and by a comparison with the description, none of which uses the source model; code repairs every fact with a single correct value, and the LLM corrects the rest in up to three rounds before the final model is simulated. On seven networks with 11 to 785 nodes, the tabular path reproduces the junction pressures of the source models, as represented internally, with a root-mean-square error (RMSE) of at most 2.1×10−4 m, which is the rounding limit. On canonical descriptions, the verbal path gave a model equal in value to the description in all 52 runs on six networks, including a real distribution zone. On the larger networks, the first extraction omitted groups of elements in every run, and correction rounds restored them.

WaterVol. 18(21)
Institute of Theoretical and Applied Informatics (PL), Polish Academy of Sciences (PL)
European Commission
Clean water and sanitation, Industry, innovation and infrastructure
Openalex Percentile: Top 19%
Water Systems and Optimization
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