Applying Factor Analysis Combined with Kriging and Information Entropy Theory for Mapping and Evaluating the Stability of Groundwater Quality Variation in Taiwan

Abstract

In Taiwan many factors, whether geological parent materials, human activities, and climate change, can affect the groundwater quality and its stability. This work combines factor analysis and kriging with information entropy theory to interpret the stability of groundwater quality variation in Taiwan between 2005 and 2007. Groundwater quality demonstrated apparent differences between the northern and southern areas of Taiwan when divided by the Wu River. Approximately 52% of the monitoring wells in southern Taiwan suffered from progressing seawater intrusion, causing unstable groundwater quality. Industrial and livestock wastewaters also polluted 59.6% of the monitoring wells, resulting in elevated EC and TOC concentrations in the groundwater. In northern Taiwan, domestic wastewaters polluted city groundwater, resulting in higher NH3-N concentration and groundwater quality instability was apparent among 10.3% of the monitoring wells. The method proposed in this study for analyzing groundwater quality inspects common stability factors, identifies potential areas influenced by common factors, and assists in elevating and reinforcing information in support of an overall groundwater management strategy.

Multivariate analysis of trace elements in shallow groundwater in Fuchu in western Tokyo Metropolis, Japan

“…The loading value of each trace element represents its contribution to the PC. HCA, on the other hand, is a multivariate statistical technique that can classify a large data set into some clusters or some smaller groups based on similarities defined as Euclidean distances among variables (Shyu et al 2011). HCA has been used to split water sample data into a small number of groups that have similar hydro-geochemical characteristics (Farnham et al 2003;Chen et al 2007).…”

“…It should be also noted that correlations among elements tend to decrease as the scales of study sites increase especially when groundwater chemistry varies markedly with scale. For example, Shyu et al (2011) reported that there are no significant correlations among heavy metals such as As, Cd, Cr, Cu, Pb, and Zn collected from 319 regional monitoring wells in Taiwan.…”

“…In order to understand the relationship and underlying common characteristics among trace elements, an appropriate data analysis technique is necessary. Multivariate analyses such as factor analysis and/or cluster analysis have been applied extensively to analyze groundwater quality data including major ions (Stetzenbach et al 1999;Kim et al 2005;Lee et al 2008;Shyu et al 2011) and trace elements (Stetzenbach et al 1999;Farnham et al 2003; Koonce et al 2006;Chen et al 2007). These techniques can simplify and clarify the groundwater quality data by reducing a number of variables into a small number of factors, hereby extracting meaningful information regarding the environmental aquatic chemistry.…”

Chemical Characteristics and Quality Assessment of Groundwater of Exploited Aquifers in Beijiao Water Source of Yinchuan, China: A Case Study for Drinking, Irrigation, and Industrial Purposes

“…Shannon entropy, as a measure of information, including disorder, dispersion, and diversification, can reduce the uncertainty of indices by predicting the output of a probabilistic event [26]. It is considered to be an objective method to determine weights.…”

“…WQIs, TOPSIS, and many other water quality assessment methods were improved by integrating the entropy [28,29]. The entropy weight can be calculated as follows [10,26,28].…”