Nonnegative Tensor Factorization for Contaminant Source Identification
V.V. Vesselinov, B.S. Alexandrov, D. O'Malley
Journal of Contaminant Hydrology2018DOI 10.1016/j.jconhyd.2018.11.010
Summary
Unsupervised Machine Learning (ML) is becoming increasingly popular for solving various types of data analytics problems including feature extraction, blind source separation, exploratory analyses, model diagnostics, etc. Here, we have developed a new unsupervised ML method based on Nonnegative Tensor Factorization (NTF) for identification of the original groundwater types (including contaminant sources) present in geochemical mixtures observed in an aquifer.