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: Predicting groundwater levels is pivotal in curbing overexploitation and ensuring effective water resource governance. However, groundwater level prediction is intricate, driven by dynamic nonlinear factors. To comprehend the dynamic interaction among these drivers, leveraging machine learning models can provide valuable insights. The drastic increase in computational capabilities has catalysed a substantial surge in the utilisation of machine learning-based solutions for effective groundwater management. The performance of these models highly depends on the selection of hyperparameters. The optimisation of hyperparameters is a complex process that often requires application-specific expertise for a skillful prediction. To mitigate the challenge posed by hyperparameter tuning’s problem-specific nature, we present an innovative approach by introducing the automated machine learning (AutoML-GWL) framework. This framework is specifically designed for precise groundwater level mapping. It seamlessly integrates the selection of best machine learning model and adeptly fine-tunes its hyperparameters by using Bayesian optimisation. We used long time series (1997-2018) data of precipitation, temperature, evaporation, soil type, relative humidity, and lag of groundwater level as input features to train the AutoML-GWL model while considering the influence of Land Use Land Cover (LULC) as a contextual factor. Among these input features, the lag of groundwater level emerged as the most relevant input feature. Once the model is trained, it performs well over the unseen data with a strong correlation of coefficient (R = 0.90), low root mean square error (RMSE = 1.22), and minimal bias = 0.23. Further, we compared the performance of the proposed AutoML-GWL with sixteen benchmark algorithms comprising baseline and novel algorithms. The AutoML-GWL outperforms all the benchmark algorithms. Furthermore, the proposed algorithm ranked first in Friedman’s statistical test, confirming its reliability. Moreover, we conducted a spatial distribution and uncertainty analysis for the proposed algorithm. The outcomes of this analysis affirmed that the AutoML-GWL can effectively manage data with spatial variations and demonstrates remarkable stability when faced with small uncertainties in the input parameters. This study holds significant promise in revolutionising groundwater management practices by establishing an automated framework for simulating groundwater levels for sustainable water resource management.

Abstract: A uranium-mineralized sandy aquifer, planned for mining by means of uranium in situ recovery (U ISR), harbors a reservoir of bacterial life that may influence the biogeochemical cycles surrounding uranium roll-front deposits. Since microorganisms play an important role at all stages of U ISR, a better knowledge of the resident bacteria before any ISR actuations is essential to face environmental quality assessment. The focus here was on the characterization of bacteria residing in an aquifer surrounding a uranium roll-front deposit that forms part of an ISR facility project at Zoovch Ovoo (Mongolia). Water samples were collected following the natural redox zonation inherited in the native aquifer, including the mineralized orebody, as well as compartments located both upstream (oxidized waters) and downstream (reduced waters) of this area. An imposed chemical zonation for all sensitive redox elements through the roll-front system was observed. In addition, high-throughput sequencing data showed that the bacterial community structure was shaped by the redox gradient and oxygen availability. Several interesting bacteria were identified, including sulphate-reducing (e.g. Desulfovibrio, Nitrospira), iron-reducing (e.g. Gallionella, Sideroxydans), iron-oxidizing (e.g. Rhodobacter, Albidiferax, Ferribacterium), and nitrate-reducing bacteria (e.g. Pseudomonas, Aquabacterium), which may also be involved in metal reduction (e.g. Desulfovibrio, Ferribacterium, Pseudomonas, Albidiferax, Caulobacter, Zooglea). Canonical correspondence analysis (CCA) and co-occurrence patterns confirmed strong correlations among the bacterial genera, suggesting either shared/preferred environmental conditions or the performance of similar/complementary functions. As a whole, the bacterial community residing in each aquifer compartment would appear to define an ecologically functional ecosystem, containing suitable microorganisms (e.g. acidophilic bacteria) prone to promote the remediation of the acidified aquifer by natural attenuation. Assessing the composition and structure of the aquifer’s native bacteria is a prerequisite for understanding natural attenuation and predicting the role of bacterial input in improving ISR efficiency.