Probabilistic assessment of potential leachate leakage from livestock mortality burial pits: A supervised classification approach using a Gaussian mixture model (GMM) fitted to a groundwater quality monitoring dataset

Abstract

Abstract After a severe epidemic of foot-and-mouth disease (FMD) in 2010–2011 in South Korea, more than 3 million livestock carcasses were promptly disposed of in a large number of on-site livestock mortality burial pits (approximately 44,000 sites) over the country. There has been significant concern regarding the potential leakage of carcass leachate from burial pits into underlying groundwater. To detect leakage, we monitored three chemical parameters (NH4+-N, Cl−, and EC) of groundwater from monitoring wells downgradient of burial pits (n\xa0=\xa0274) in 2011. The monitored data were applied as the prediction set to a supervised classification scheme using the Gaussian mixture model (GMM) which involves chemical analysis of both the leachate effluent and background groundwater (as the training set). The GMM was tested to the different data distributions of the training set and resulted in statistically accurate models (with 10-fold CV error