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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Acknowledgments
The authors sincerely appreciate financial supports for the project (NSC 100-2221-E214-011) from Taiwan’s National Science Council for the work.
The authors also deeply appreciate the anonymous reviewers for their insightful comments and suggestions.
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Wu, E.MY., Tsai, C.C., Cheng, J.F. et al. The Application of Water Quality Monitoring Data in a Reservoir Watershed Using AMOS Confirmatory Factor Analyses. Environ Model Assess 19, 325–333 (2014). https://doi.org/10.1007/s10666-014-9407-5
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DOI: https://doi.org/10.1007/s10666-014-9407-5