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: 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: 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.

Abstract: Since the coming into force of the European Council Directive 51/2013 EURATOM and its transposition into the Spanish legislation, the presence of radioactive substances in drinking waters must be kept under surveillance to ensure that the health protection requirements are met. Driven by this regulatory framework, in an attempt to know the starting point from which to design surveillance plans, the groundwaters intended for human consumption of Castilla y León (Spain) have been radiologically characterised by using both low-level γ-ray and α-particle spectrometry to determine the activity concentration of the natural radionuclides needed to account for the indicative dose estimation. This extensive research has comprised the radiological characterisation of more than 400 drinking water samples from one of the European Union’s largest regions. Furthermore, the gross α and gross β activities have been analysed. Results showed a high geographical variability that can be related to the hydrogeological formations where the groundwaters come from. The uranium isotopes, 234U and 238U, are the main radionuclides present in the analysed drinking waters reaching values up to 2000 mBq/L, in the southwestern and western of Castilla y León, where U-rich minerals are part of the host rock. High 210Pb and 226,228Ra occurrences are found in the low permeability igneous and metasedimentary hydrogeological formations of Salamanca province. From a public health protection point of view, 4.4% of the total drinking water samples from intakes exceeded the Indicative Dose parametric value of 0.1 mSv, which is a not negligible number of samples, being very likely related to granitic and metamorphosed host rock under specific local conditions. This fact highlights the need for research and consideration of special surveillance of the groundwaters from these areas.

Abstract: It is well known that rainwater harvesting (RWH) can augment water supply and reduce stormwater pollutant discharges. Due to the lack of continuous 2D modelling of urban flood coverage and its associated damage, the ability of RWH to reduce urban flood risks has not been fully evaluated. Literature suggests that small distributed storage spaces using RWH tanks will reduce flood damage only during small to medium flooding events and therefore cumulative assessment of their benefits is needed. In this study we developed a new integrated modelling framework that implements a semi-continuous simulation approach to investigate flood prevention and water supply benefits of RWH tanks. The framework includes a continuous mass balance simulation model that considers antecedent rainfall conditions and water demand/usage of tanks and predicts the available storage prior to each storm event. To do so, this model couples a rainfall-runoff tank storage model with a detailed stochastic end-use water demand model. The available storage capacity of tanks is then used as a boundary condition for the novel rapid flood simulation model. This flood model was developed by coupling the U.S. EPA Storm Water Management Model (SWMM) to the Cellular-Automata Fast Flood Evaluation (CA-ffé) model to predict the inundation depth caused by surcharges over the capacity of the drainage network. The stage-depth damage curves method was used to calculate time series of flood damage, which are then directly used for flood risk and cost-benefit analysis. The model was tested through a case study in Melbourne, using a recorded rainfall time series of 85 years (after validating the flood model against 1D-2D MIKE-FLOOD). Results showed that extensive implementation of RWH tanks in the study area is economically feasible and can reduce expected annual damage in the catchment by up to approximately 30 percent. Availability of storage space and temporal distribution of rainfall within an event were important factors affecting tank performance for flood reduction.