Assessment of surface water quality of Songkhram River (Thailand) using environmetric techniques

Abstract

Environmetric techniques such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis were applied for the assessment of spatial and temporal variations of a large complex water-quality data set of the Songkhram River Basin, generated during 15 years (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)) by monitoring of 17 parameters at 5 different sites. Hierarchical CA grouped five sampling sites into three clusters, i.e. upper stream (US), middle stream (MS) and lower stream (LS) sites, based on water-quality characteristics. FA/PCA applied to the data sets thus obtained resulted in six latent factors explaining 80.80, 73.95 and 73.78% of the total variance in water-quality data sets of LS, MS and US areas, respectively. This study highlights the usefulness of multivariate statistical assessment of complex databases in the identification of pollution sources and to better comprehend the spatial and temporal variations for effective river water-quality management.

Assessment of surface water quality using multivariate statistical techniques: case study of the Nampong River and Songkhram River, Thailand

“…River water quality data sets were subjected to four multivariate techniques: CA, PCA, FA, and DA (Reghunath et al 2002;Zhou et al 2007;Boyacioglu and Boyacioglu 2007;Shrestha et al 2008;Zhang et al 2011;Shrestha and Muangthong 2014;Muangthong 2015). DA was applied to raw data, whereas PCA, FA, and CA were applied to experimental data, standardized through z scale transformation to avoid misclassifications arising from the different orders of magnitude of both numerical values and variance of the parameters analyzed (Liu et al 2003;Simeonov et al 2003).…”

“…Application of multivariate statistical techniques such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA), and discriminant analysis (DA) has increased tremendously in recent years for analyzing environmental data and drawing meaningful information (Reghunath et al 2002;Zhou et al 2007;Boyacioglu and Boyacioglu 2007;Coletti et al 2010;Shihab and Abdul Baqi 2010;Guangjia et al 2010;Batayneh and Zumlot 2012;Shrestha and Muangthong 2014). Application of different multivariate statistical techniques helps in the interpretation of complex data matrices to better understand the water quality and ecological status of the studied systems.…”

A Time Series Model Comparison for Monitoring and Forecasting Water Quality Variables

“…In fact, the Lunj-Box test for the non-correlation hypothesis does not reject, using up to 30 different lags in all the locations under study, and Figure 4 presents the histograms of the residuals that resemble the normal curve. 9.599 0.00 0.00 9.983 0.00 0.00 9.673 0.00 0.00 9.559 0.00 0.00 9.532 0.00 0.00 β 3 9.212 0.00 0.00 9.546 0.00 0.00 9.138 0.00 0.00 9.504 0.00 0.00 9.737 0.00 0.00 β 4 8.753 0.00 0.00 9.001 0.00 0.00 9.356 0.00 0.00 9.134 0.00 0.00 9.014 0.00 0.00 β 5 8.313 0.00 0.00 9.262 0.00 0.00 8.555 0.00 0.00 8.688 0.00 0.00 8.753 0.00 0.00 β 6 8.362 0.00 0.00 7.771 0.00 0.00 7.270 0.00 0.00 7.847 0.00 0.00 7.939 0.00 0.00 β 7 7.273 0.00 0.00 8.164 0.00 0.00 7.271 0.00 0.00 8.576 0.00 0.00 8.376 0.00 0.00 β 8 7.087 0.00 0.00 8.844 0.00 0.00 6.556 0.00 0.00 7.668 0.00 0.00 8.260 0.00 0.00 β 9 7.971 0.00 0.00 7.941 0.00 0.00 5.800 0.00 0.00 7.166 0.00 0.00 7.319 0.00 0.00 β 10 7.410 0.00 0.00 7.844 0.00 0.00 7.673 0.00 0.00 7.561 0.00 0.00 8.477 0.00 0.00 β 11 8.030 0.00 0.00 8.968 0.00 0.00 9.737 0.00 0.00 9.673 0.00 0.00 9.408 0.00 0.00 β 12 9.859 0.00 0.00 9.946 0.00 0.00 9.418 0.00 0.00 9.025 0.00 0.00 9.207 0.00 0.00 Furthermore, with the exception of the CAR location, the residuals of the calibration model do not reject (at a 1% significance level) the normality assumption using the Jarque-Bera test or the Kolmogorov-Smirnov test; the K-S p-values are presented in Table 5.…”

“…In [4], linear regression, principal component analysis, and cluster analysis were applied to analyze a voluminous and complex dataset of Vishav stream, which had been acquired during 1-year monitoring program of 21 parameters at five different sites. In [5], cluster analysis, principal component analysis, factor analysis, and discriminant analysis were used for the assessment of spatial and temporal variations of a large complex water-quality dataset of the Songkhram River Basin, generated during 15 years (1995-2009) by monitoring 17 parameters at five different sites. In [6], generalized additive models of location, scale, and shape (GAMLSS) were applied to characterize model uncertainty, due to incomplete understanding of physical processes, in an Atlantic coastal plain watershed system.…”

Water Quality Assessment of Al-Gharraf River, South of Iraq Using Multivariate Statistical Techniques

“…Such tools facilitate the identification of possible factors that influence water quality and can aid in the reliable management of water resources as well as a solution to pollution problems [14,15,16].…”