Assessment of physically-based and data-driven models to predict microbial water quality in open channels

查看全文 作　　者：Minyoung [1]Kim;Charles [2]P.Gerba;Christopher [3]Y.Choi

机构地区：[1]Agricultural Safety Engineering Division,Department of Agricultural Engineering,National Academy of Agricultural Science,Rural Development Administration(Korea);[2]Department of Soil,Water,and Environmental Science,the University of Arizona;[3]Department of Agricultural and Biosystems Engineering,the University of Arizona

出　　处：《Journal of Environmental Sciences》索引2010年第22卷第6期,共7页

摘　　要：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 cm 3 /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. 关键词：数据驱动水质参数微生物评估人工神经网络物理模型明渠预测

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Assessment of physically-based and data-driven models to predict microbial water quality in open channels

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