Hermes Luca, Artelt André, Vrachimis Stelios G, Polycarpou Marios M, Hammer Barbara
Bielefeld University, Inspiration 1, 33615 Bielefeld, NRW Germany.
KIOS Research and Innovation Center of Excellence, University of Cyprus, Panepistimiou 1, Aglantzia, 2109 Nicosia, Cyprus.
SN Comput Sci. 2025;6(5):522. doi: 10.1007/s42979-025-04008-y. Epub 2025 Jun 4.
Ensuring high-quality drinking water is a critical responsibility of water utilities, with chlorine being the main disinfectant typically used. Accurate estimation of chlorine concentrations in the dynamic environment of water distribution networks (WDNs) is essential to ensure safe water supply. This work introduces a comprehensive and carefully created benchmark for training and evaluation of chlorine concentration estimation methodologies in WDNs. The benchmark includes a diverse dataset of 18,000 scenarios of the widely studied 'Hanoi', 'Net1', and the more recent and complex 'CY-DBP' water networks, featuring various chlorine injection patterns to capture diverse physical dynamics. To provide baseline evaluations, we propose and evaluate two neural surrogate models for chlorine state estimation: a physics-informed Graph Neural Network (GNN) and a physics-guided Recurrent Neural Network (RNN).
确保高质量饮用水是供水企业的一项关键职责，氯是通常使用的主要消毒剂。准确估计配水管网（WDN）动态环境中的氯浓度对于确保安全供水至关重要。这项工作引入了一个全面且精心创建的基准，用于训练和评估WDN中氯浓度估计方法。该基准包括一个由18000个场景组成的多样化数据集，这些场景来自广泛研究的“河内”、“Net1”以及更新的复杂“CY-DBP”水网络，具有各种氯注入模式，以捕捉不同的物理动态。为了提供基线评估，我们提出并评估了两种用于氯状态估计的神经替代模型：一种基于物理知识的图神经网络（GNN）和一种基于物理引导的循环神经网络（RNN）。
