Agricultural Safety Engineering Division, Department of Agricultural Engineering, National Academy ofAgricultural Science, Rural Development Administration, 249 Seodun-dong, Gwonson-gu, Suwon, 441-707, Korea.
J Environ Sci (China). 2010;22(6):851-7. doi: 10.1016/s1001-0742(09)60188-1.
In the present study, a physically-based hydraulic modeling tool and a data-driven approach using artificial neural networks (ANNs) were evaluated for their ability to simulate the fate and transport of microorganisms in a water system. To produce reliable data, a pipe network was constructed and a series of experiments using a fecal coliform indicator (Escherichia coli 15597) was conducted. For the physically-based model, morphological (pipe size, link length, slope, etc.) and hydraulic (flow rate) conditions were used as input variables, and for ANNs, water quality parameters (conductivity, pH, and turbidity) were used. Both approaches accurately described the fate and transport of microorganisms (physically-based model: correlation coefficient (R) in the range of 0.914-0.977 and ANNs: R in the range of 0.949 - 0.980), with the exception of one case at a low flow rate (q = 31.56 cm3/sec). This study also indicated that these approaches could be complementarily utilized to assess the vulnerability of water facilities and to establish emergency plans based on hypothetical scenarios.
在本研究中，评估了一种基于物理的水力建模工具和一种使用人工神经网络 (ANNs) 的数据驱动方法，以评估它们模拟水系统中微生物命运和迁移的能力。为了生成可靠的数据，构建了一个管网，并进行了一系列使用粪大肠菌群指示剂（Escherichia coli 15597）的实验。对于基于物理的模型，形态（管道尺寸、链路长度、坡度等）和水力（流速）条件被用作输入变量，而对于 ANNs，则使用水质参数（电导率、pH 值和浊度）。这两种方法都准确地描述了微生物的命运和迁移（基于物理的模型：相关系数 (R) 在 0.914-0.977 的范围内，而 ANNs：R 在 0.949-0.980 的范围内），除了一个低流速（q = 31.56 cm3/sec）的情况。本研究还表明，这些方法可以互补使用，以评估水设施的脆弱性，并根据假设情景制定应急预案。
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