首页 | 本学科首页   官方微博 | 高级检索  
     检索      


The Application of Water Quality Monitoring Data in a Reservoir Watershed Using AMOS Confirmatory Factor Analyses
Authors:Edward Ming-Yang Wu  Chia Cheng Tsai  Juey Fu Cheng  Shu Lung Kuo  Wei Ting Lu
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
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.
Keywords:
本文献已被 SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号