Improving prediction of groundwater quality in situations of limited monitoring data based on virtual sample generation and Gaussian process regression

The increasing pollution of aquifers by human activities over recent decades poses a threat to drinking water safety. While Gaussian Process Regression (GPR) is a robust tool for predicting and monitoring water quality, its effectiveness is hindered limitations of available data on model training and validation, known as the "small sample problem". Various attempts to resolve this problem include virtual sample generation (VSG). This study aimed to increase the accuracy of GPR for predicting water quality in situations of limited datasets. Three VSG methods, namely Multi Distribution Mega-Trend Diffusion (MD-MTD), Generative Adversarial Network (GAN), and t-distributed stochastic nearest neighbor embedding (t-SNE) were compared for enhancing the accuracy of GPR model prediction of Strontium (Sr