Bayesian deduction for redundancy detection in groundwater quality monitoring networks

A new methodology for designing a network for monitoring ambient, long‐term groundwater quality is presented in this paper. The methodology is based on a sparse Bayesian learning approach known as a relevance vector machine (RVM) which produces probabilistic predictions that quantify the uncertainty in both the data and the model parameters. A reliable and parsimonious network configuration that is pertinent to the physics of the case study, revealed through understanding of the information content of the available data, is sought through application of the RVM. The methodology has been employed to reduce redundancy in the network for monitoring nitrate (NO 3 − ) in the West Bank Palestinian National Authority aquifers to illustrate the potential for use of RVMs in optimal groundwater monitoring and to explore possible trade‐offs between different monitoring objectives, e.g., monitoring cost versus uncertainty in groundwater. A sparse monitoring network configuration produced by the RVM‐based model indicates that only 32% of the existing monitoring sites in the aquifer are sufficient to characterize the nitrate state. Proof of correctness and accuracy using rigorous statistical tests is presented.