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Research on System of Water Quality Assessment of Secondary Watersupply in Urban Distict

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Tutor: YuanYiXing
School: Harbin Institute of Technology
Course: Municipal Engineering
Keywords: secondary water supply,BP neural network,multivariate linearregression,warning m
CLC: TU991.21
Type: Master's thesis
Year:  2012
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With the development of the city, the expansion of water supply networks becomes increasingly complex, at the same time, the water quality problems of secondary water supply have become increasingly prominent. The pollution of water quality in secondary water supply has affected people¡¯s health, so the study of water quality problems of secondary water supply is extremely urgent.The standards of uban drinking water increase from35to106items, which represents the improvement of the requirement of national drinking water quality. and from July1,2012, not only drinking water is required to meet the national standards, water quality of residential customers is also asked to reach the national standards, so we should pay much attention to the secondary water pollution.This paper is carried out aiming at the water quality situation in urban areas, including the research on service life of pipes, pipe diameter and the water situation of residents. The detailed information we collected is to the advantage of the layout of monitoring points and determination on sampling plan. In the process of water quality monitoring, we selected seven water quality indicators:pH, temperature, turbidity, total iron, chlorine, TOC and BDOC. We mainly focus on the analysis of the law of seven indicators with seasonal variation, and discuss the fluctuations of seven indicators. Finally, based on these water quality indicators, we establish relative biological models.After establishing the correlation analysis model of water quality indicators, using SPSS software to analyze the correlation coefficient of the relevant indicators, we are able to draw the inner relationship of the two water quality indicators. We mainly focus on the analysis of positive correlation between BDOC and turbidity, positive correlation between BDOC and total iron, negative correlation between BDOC and free chlorine, and establish the correlation of organic pollution.We establish BDOC prediction model, using multiple linear regression model and BP neural network prediction model. Using three-layered neural network structure and25neurons in the network structure, we perform the prediction. After establishing modeling, through error analysis we compare the accuracy of the results using two methods. And the accuracy of the BP neural network is93.42%. The accuracy of the linear regression is82.93%. In this way we are able to compare the pros and cons of methods so as to choose a model which is of higher prediction accuracy to guide practice.Using Microsoft C#language and SQLserver2008as the platform, we develop a warning system of the water evaluation applying fuzzy evaluation model to evaluate the monitoring of water quality. Thus we can carry out real-time feedback regulation in the process of water quality monitoring, which is good for us to track the situation of water quality. Meanwhile, we establish a database to conduct a comprehensive analysis of water quality.It is of great significance to solve the problem of the pollution of secondary water supply in our real life. This text is proposed to put forward relevant countermeasures in terms of drinking water pollution in secondary water supply.
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