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Supervised Self Organizing Maps for Exploratory Data Analysis of Running Waters Based on Physicochemical Parameters: a Case Study in Chiang Mai, Thailand

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Sila Kittiwachana and Kate Grudpan

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This report demonstrated the use of a supervised self organizing map (SOM) for exploratory analysis of running waters based on their chemical criteria. Water samples from 10 different sites, representing 4 different water types – streams, a river, an irrigation canal and a sewage canal – were collected from some areas in Chiang Mai, Thailand, during 8-month period from May to December and analyzed for 16 physicochemical parameters. The samples were categorised into 8 classes (the 8 months from May to December) and 10 classes (the 10 sampling sites). This information was incorporated into the modeling using a supervised SOM methodology. The results were visualized using supervised colour shading and a unified distance matrix (U-matrix). The supervised SOM improved the correlation among the samples within group. It was possible to reveal the water sample clusters, either when organized according to the sampling times or sites. Moreover, all of the variation could be used for the analysis, eliminating the need to choose the specifi dimensions or the number of principal components (PCs).

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exploratory data analysis, supervised self organizing map (SOM), artifiial neural networks (ANNs), principal component analysis (PCA), water analysis.