Jina Yin

TL;DR: In this article, a machine learning-based groundwater ensemble modeling framework was proposed to predict groundwater storage change and improve overall model predicting reliability. But, the model is not able to produce consistently reliable predictions across the basin.

TL;DR: The study found that Bayesian set pair analysis builds robust ensemble surrogates and accounts for model prediction uncertainty and the ensemble-surrogate-assisted optimization model provides stable and reliable solutions while considerably alleviating computational burden.

TL;DR: This study presents an ensemble surrogate modeling approach to address uncertainty in surrogate model predictions for saltwater intrusion mitigation designs using a hypothetical horizontal scavenger well and shows that the developed surrogate models perform well and have strong predictive capabilities with respect to the solute transport model response, while considerably reducing computation time.

TL;DR: In this article, the authors studied the permeability of a silty clay and analyzed the combined influence of bulk density and water temperature on permeability, and they showed that when the dry bulk density of the silty clays increases from 1.4 to 1.6 g/cm3, the hydraulic conductivity decreases exponentially from 10−4 to 10−5cm/s (at 10, 15, 20 and 25 °C).

TL;DR: In this article, the authors present challenges in managing groundwater resources because of spatiotemporally variable pumping activities as well as complex subsurface hydrogeology, and excessive water exploitat...