Non-linear visualization and analysis of large water quality data sets: a model-free basis for efficient monitoring and risk assessment

Abstract

Environmental monitoring programs provide large multivariate data sets that usually cover considerable spatial and temporal variabilities. The apparent complexity of these data sets requires sophisticated tools for their processing. Usually, fixed schemes are followed, including the application of numerical models, which are increasingly implemented in decision support systems. However, these schemes are too rigid with respect to detecting unexpected features, like the onset of subtle trends, nonlinear relationships or patterns that are restricted to limited sub-samples of the total data set. In this study, an alternative approach is followed. It is based on an efficient nonlinear visualization of the data. Visualization is the most powerful interface between computer and human brain.

Predicting seasonal and hydro-meteorological impact in environmental variables modelling via Kalman filtering

“…In several works, multivariate statistical analyses are applied to sets of water quality variables, usually quantitative analytical data consisting of physico-chemical variables. If the goal is to investigate water quality evaluation in its timespace variations as in Helena et al (2000), or the natural and anthropogenic origins of contaminants in surface or ground water as in Ato et al (2010), the most suitable and applied approach is the principal components analysis (Liu et al 2003;Lischeid 2009;Varol and Sen 2009). In some practical studies, there is data available from a group of sample sites, usually water monitoring sites, which is useful to perform several statistical methodologies: for instance, correlation analysis parametric and non-parametric tests (Elhatip et al 2008).…”

Multivariate Analysis of Groundwater-Quality Time-Series Using Self-organizing Maps and Sammon’s Mapping

“…This is difficult to visualize and small differences between system states are disregarded. Some authors combine SOMs with nonlinear projection methods to capture the temporal aspect of system state development (Bernataviciene et al 2006;Mustonen et al 2008;Lischeid 2009). To emphasize the temporal resolution and visualization of variation in the original data set, especially over time, the output of the SOMs were further subjected to the Sammon's mapping algorithm (Sammon 1969).…”

Multivariate analysis of physico-chemical and microbial parameters of surface water in Kelani river basin

“…The coordinates of localities were determined by GPS (Hand-held Gamin eTrex 30 GPS receiver). A PCA reduces the number of dimensions in the data set, while not losing detail or underlying patterns observed in some or all of the observation wells (Lischeid, 2009). The methodology is based on correlation coefficients of the data matrix.…”