Tanja Liesch

TL;DR: In this paper, the authors used nonlinear autoregressive networks with exogenous inputs (NARX) to obtain groundwater level forecasts for several wells in southwest Germany, and the results are promising and indicate an outstanding suitability of NARX for groundwater level predictions with such a small set of inputs in all three aquifer types.

TL;DR: Results show that 1.6% of all water samples exceed the German legal limit for drinking water (10 μg/L) and there is a clear relation between agricultural land-use and low-level uranium concentrations, indicating that fertilizers generate a measurable but low background of uranium in groundwater.

TL;DR: It was showed that shallow neural networks, such as NARX, should not be neglected in comparison to DL techniques especially when only small amounts of training data are available, where they can clearly outperform LSTMs and CNNs; however, LSTM and CNN's might perform substantially better with a larger dataset, where DL really can demonstrate its strengths, which is rarely available in the groundwater domain though.

TL;DR: Investigation of spatiotemporal fluctuations of surface and sub-surface water source partitioning and mixing and nitrogen (N) contamination in the urban water cycle of As-Salt, Jordan found that septic waste from leaky sewers is the main contributor of nitrate contamination.

TL;DR: In this article, the authors investigated the possible propagation of uncertainty of the chosen interpolation method on the calculation of the estimated vertical groundwater exchange between the aquifers and validated the results with eco-hydrogeological data including the comparison between calculated groundwater depths and geographic locations of karst springs, wetlands and surface waters.