Abstract
This paper presented a case study where a novel method based on grey theory analysis was applied to mining environmental monitoring database to extract the patterns of groundwater contamination. The grey model (GM) was employed to predict the arsenic contamination of groundwater from monitoring data sets with high level of arsenic in Chianan Blackfoot disease region during the period of 2009 and 2012. The results indicated that the minimum mean absolute percentage errors of 2.98 could be achieved by applying grey model GM(1, 1). Compared to the traditional numerical analysis methods, grey model only required a small amount of data and the prediction results were even better than typical numerical methods. According to the results, the grey model could predict the arsenic contamination variation as the data was insufficient.
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