Continuous Bayesian networks for probabilistic environmental risk mapping

Bayesian networks (BNs) are being increasingly applied to environmental research. Nonetheless, most of the literature related to environmental sciences use discrete or discretized data, which entails a loss of information. We propose a novel methodology based on continuous BNs to predict the probability that surface waters do not meet the standards, in relation to nitrate concentration, established by the European Water Framework Directive. In order to achieve our purpose, a Tree Augmented Naive Bayes (TAN), was developed and applied to estimate and map the risk of failing to meet the European standards established. The TAN models were tested by means of the k-fold cross validation method. The results revealed that the TAN model performed proper risk maps and suggested that poor water quality is highly probable in watersheds dominated by irrigated herbaceous crops. On the contrary, “good surface water status” is more likely to occur in areas where forest is notably present.