Abstract: The crucial role which groundwater resource plays in our environment and the overall well-being of all living things can not be underestimated. Nonetheless, mismanagement of resources, over-exploitation, inadequate supply of surface water and pollution have led to severe drought and an overall drop in groundwater resources’ levels over the past decades. To address this, a groundwater flow model and several mathematical data-driven models have been developed for forecasting groundwater levels. However, there is a problem of unavailability and scarcity of the on-site input data needed by the data-driven models to forecast the groundwater level. Furthermore, as a result of the dynamics and stochastic characteristics of groundwater, there is a need for an appropriate, accurate and reliable forecasting model to solve these challenges. Over the years, the broad application of Machine Learning (ML) and Artificial Intelligence (AI) models are gaining attraction as an alternative solution for forecasting groundwater levels. Against this background, this article provides an overview of forecasting methods for predicting groundwater levels. Also, this article uses ML models such as Regressions Models, Deep Auto-Regressive models, and Nonlinear Autoregressive Neural Networks with External Input (NARX) to forecast groundwater levels using the groundwater region 10 at Karst belt in South Africa as a case study. This was done using Python Mx. Version 1.9.1., and MATLAB R2022a machine learning toolboxes. Moreover, the Coefficient of Determination (R2);, Root Mean Square Error (RMSE), Mutual Information gain, Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and the Mean Absolute Scaled Error (MASE)) models were the forecasting statistical performance metrics used to assess the predictive performance of these models. The results obtained showed that NARX and Support Vector Machine (SVM) have higher performance metrics and accuracy compared to other models used in this study.

Abstract: Batch and column experiments were conducted to determine whether zerovalent iron (ZVI) and sulfate reducing bacteria (SRB) can function synergistically and accelerate pollutant removal. Batch experiments suggest that combining ZVI with SRB can enhance the removal of U(?) synergistically. The removal rate of U(?) in the ZVI+SRB combining system is obviously higher than the total rate of ZVI system and SRB system with a difference of 13.4% at t=2 h and 29.9% at t=4 h. Column experiments indicate that the reactor filled with both ZVI and SRB biofilms is of better performance than the SRB bioreactor in wastewater basification, desulfurization and U(?) fixation. The results imply that the ZVI+SRB permeable reactive barrier may be a promising method for treating subsurface uranium contamination.

Abstract: Spain has deep roots in the underground. The territory of myth and legend, of cave and tunnel, has existed in Spain since that ancient time overwhelmed with shreds of fog, where all was myth around heros like Túbal Hércules, Gárgoris and Abidis. The underground evokes strong links with life and death, light and darkness, and has served as a source of inspiration for art through the centuries. The history of tunnels in Spain reflects the mosaic of cultures that have inhabited Iberia from prehistoric times till today. This contribution on the subterranean History of Spain traces the country’s heritage in the form of natural caves, troglodyte dwellings, mining, crypts, galleries in fortresses and castles, aqueducts, qanats, cellars, and other landmarks.

Abstract: Demonstration des Potentials der Nitrat-Isotopenanalytik für die Strategieentwicklung der Sanierung Nitrat-belasteter Brunnen

Abstract: Across the globe, the damage caused by urban floods has increased manifold. The unchecked development has encroached the natural drainage, and the conventional drainage systems are inadequate in handling the augmented hydrological response. To counter this, a variety of approaches with the ability to adjust within the constraints of complex environments by managing surface runoff are being widely investigated and applied worldwide. These can put the flood water to better use, and the ecological balance may get restored. This review discusses recent progress made in the area of Green Infrastructure (GI), modelling tools that help in stormwater management, vulnerability analysis and flood risk assessment. Different ways of handling the problem are summarized through an extensive literature survey. The gaps and barriers that impede the implementation of stormwater management solutions and strategies for further improvement have also been presented. A case study of Gurugram city, India depicting the challenges being faced by urban flooding and the possible solutions through an expert survey is also presented.