The Application of Water Quality Monitoring Data in a Reservoir Watershed Using AMOS Confirmatory Factor Analyses |
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Authors: | Edward Ming-Yang Wu Chia Cheng Tsai Juey Fu Cheng Shu Lung Kuo Wei Ting Lu |
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Institution: | 1. Department of Civil and Ecological Engineering, I-Shou University, No. 1, Sec. 1, Syecheng Rd., Daishu District, Kaohsiung City, 840, Taiwan, Republic of China 2. Department of Marine Environmental Engineering, National Kaohsiung Marine University, Kaohsiung City, 811, Taiwan, Republic of China 4. International Wave Dynamics Research Center, National Cheng Kung University, Tainan City, 901, Taiwan, Republic of China 3. Kelee Environmental Consultant Corporation, 6F.-2, No. 288-8, Sinya Road, Kaohsiung City, 806, Taiwan, Republic of China
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Abstract: | This study investigates six water quality monitoring stations in the watershed of the Feitsui Reservoir. It uses nine parameters of water quality collected in an interval of two and half years for factor analyses, which was first conducted to determine four types of factors, respectively, those for organic pollution, eutrophication, seasonal influence, and sediment pollution. The analysis results effectively help to determine water quality in the watershed of the reservoir. The authors reutilize analysis of moment structures (AMOS) to acquire further results in order to confirm the goodness of fit of the previous factor analysis model. During the confirmation, we examine the hypothesized orthogonal results as well as utilize oblique rotation to explore the goodness of fit of the reflective indicators of the orthogonal rotation. As shown in the algorithm results, as long as the covariance curve is included in the four factors, no related issues are detected in the goodness of fit of reflective indicators and interior and external quality is reported with excellence. The orthogonal model, thus, stands. Additionally, when the analysis of structural equation modeling (SEM) is conducted, sample data mismatches the hypotheses of multivariate normality. Therefore, this study adopts the generalized least square (GLS) for an algorithm. Research results of this study have been submitted to the reservoir management authorities in Taiwan for the improvement of statistical application and strategic evaluation of water quality monitoring data in order to strengthen the managerial effectiveness of water quality in watersheds. |
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