确定性模型得到了广泛的应用 用于预测配水系统中的水质 但它们的校准需要大量的时间 和准确的数据集 参数。在本研究中，替代数据驱动 基于人工神经网络的建模方法 神经网络（ANN）被用来 预测两个重要参数的时间变化 水质特征，氯 残余物和生物量浓度。 作者考虑了三种类型的神经网络 算法。其中，莱文伯格·马夸特 算法提供了最佳的搜索结果 基于遗传算法的余氯和生物量预测 无错误且“嘈杂”的数据。人工神经网络模型 在这里开发可以产生水质 实时管道系统的场景有助于 公用设施决定了低氯的弱点 残留和高生物量浓度以及 选择最佳的补救策略。包括23个参考文献、表格和图表。

Deterministic models have been widely used to predict water quality in distribution systems, but their calibration requires extensive and accurate data sets for numerous parameters. In this study, alternative data-driven modeling approaches based on artificial neural networks (ANNs) were used to predict temporal variations of two important characteristics of water quality, chlorine residual and biomass concentrations. The authors considered three types of ANN algorithms. Of these, the Levenberg-Marquardt algorithm provided the best results in predicting residual chlorine and biomass with error-free and "noisy" data. The ANN models developed here can generate water quality scenarios of piped systems in real time to help utilities determine weak points of low chlorine residual and high biomass concentration and select optimum remedial strategies.Includes 23 references, tables, figures.