Data mining and multivariate statistical analysis for ecological system in coastal waters

Abstract

In this study, data mining using box plots and multivariate statistical analysis using factor analysis are employed for a spatio-temporal analysis of coastal water quality data from Tolo Harbour, Hong Kong. The analysis of box plots reveals pronounced spatial heterogeneity of the parameters studied. The spatial analysis clearly shows monitoring station TM2 in the Harbour Subzone to be most susceptible to eutrophication with the highest nutrient and algal biomass concentrations. The factor analysis brings to light dominant parameters to the ecological system under the coastal marine environment. The temporal analysis confirms the considerable decline in nutrient levels in recent years. In spite of this decline, the factor analysis indicates that nutrient processes play an important role even in recent years, suggesting an adequate supply of nutrients. It seems that they are being released from sources other than known point sources, possibly from nutrients accumulated in the sediments, necessitating steps to be undertaken for their control also. This study demonstrates the use of data mining techniques in the ecological system in Tolo Harbour.

Primary consumers as bioindicator of nitrogen pollution in lake planktonic and benthic food webs

“…The overload of these nutrients causes eutrophication and further results in the damage of ecosystem structures and functions (Smith et al, 1999;Viana et al, 2011). An initial assessment of the trophic state and the tracing of the sources of nutrient loadings into aquatic ecosystems are both important in the process of understanding the detrimental effects of nutrient input (Vander Zanden et al, 2005;Park et al, 2005;Chau and Muttil, 2007). The most commonly used methods in assessing the trophic state of lakes are the measurement of the total phosphorus, total nitrogen, Secchi depth, and Chlorophyll a from the water column (Carlson, 1977;Smith et al, 1999;Muttil and Chau, 2006;Weilhoefer, 2011).…”

Chemometry use in the evaluation of the sanya bay water quality

“…In recent years, chemometrics such as cluster analysis (CA) and principal component analysis (PCA) have been effectively employed to evaluate the temporal and spatial characteristics of coastal water quality (KUPPUSAMY; GIRIDHAR, 2006;ZHOU et al, 2007a;ZHOU et al, 2007b;YUNG et al, 2001;YEUNG, 1999;WANG et al, 2006;KOTTI et al;2005, WU;WANG, 2007;MUTTIL, 2007;SIMEONOV et al, 2003). Cluster analysis (CA) is called the unsupervised pattern recognition method, grouping objects into classes (clusters) so that objects within a class are similar to each other but different from those in other classes (ALBERTO et al, 2001).…”

Visualization Investigation on the Marine Data with Multivariate Statistical Analysis Methods

“…Marine data also has time attribute and data collected at different time are multivariate variable [12]. We want to do the study on the visualization work of the marine data in both the special aspect and the time aspect.…”