Transferability of Machine Learning Models for Geogenic Contaminated Groundwaters

Machine learning models show promise in identifying geogenic
contaminated
groundwaters. Modeling in regions with no or limited samples is challenging
due to the need for large training sets. One potential solution is
transferring existing models to such regions. This study explores
the transferability of high fluoride groundwater models between basins
in the Shanxi Rift System, considering six factors, including modeling
methods, predictor types, data size, sample/predictor ratio (SPR),
predictor range, and data informing. Results show that transferability
is achieved only when model predictors are based on hydrochemical
parameters rather than surface parameters. Data informing, i.e., adding
samples from challenging regions to the training set, further enhances
the transferability. Stepwise regression shows that hydrochemical
predictors and data informing significantly improve transferability,
while data size, SPR, and predictor range have no significant effects.
Additionally, despite their stronger nonlinear capabilities, random
forests and artificial neural networks do not necessarily surpass
logistic regression in transferability. Lastly, we utilize the t-SNE algorithm to generate low-dimensional representations
of data from different basins and compare these representations to
elucidate the critical role of predictor types in transferability.