Geophysical datasets, namely, electrical resistivity, induced polarization, magnetic susceptibility were measured and aeromagnetic interpreted on the Subika concession of Newmont Ghana Gold Limited in order to delineate and model the zone of gold mineralization. The electrical resistivity and induced polarization results delineated the gold mineralized zone as a narrow discrete resistor (>4900 Ωm) and high chargeability (>7.2 ms) zone with a strike length of about 2 km. A strip log plots of 12 diamond drilled core samples showed the zone of mineralization with high alteration and low magnetic susceptibility. Analysis of laboratory magnetic susceptibility measured on selected samples from the pit and selected core samples from the mineralized zones showed a negative correlation between the magnetic susceptibility and gold grades with a correlation coefficient of −0.2 and −0.74, respectively. Similarly, magnetic depth slice produced from the 3D model of the aeromagnetic data depicts the zone of gold mineralization with a low magnetic signature. The zone mineralization was also observed to be striking in the north-east direction. Mineralogical analyses of rock samples revealed alteration processes such as carbonization, sericitization, silicification, chloritization and sulphidation resulting from hydrothermal alteration which accounted for the low magnetic and the magnetic susceptibility signatures of the mineralization zones. The strong resistivity and chargeability response of the mineralized zones are therefore attributed to the silicification and sulphidation alteration, respectively. The results show the effectiveness of applying integrated geophysical, petrophysical as well as mineralogical study in revealing gold mineralization potential of the Subika deposit and the Sefwi belt as a whole.

Physical models have long been employed for groundwater level (GWL) prediction. Recently, artificial intelligence (AI), particularly neural networks (NNs), has gained widespread use in forecasting GWL. Forecasting of GWL is essential to enable the analysis, quantifying, and management of groundwater. This systematic review investigates the application of NNs for GWL prediction, focusing on the architectures of the various NN models employed. The study utilizes the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology to screen and synthesize relevant scientific articles. Various NN architectures, such as artificial neural networks (ANNs), feedforward neural networks (FFNNs), backpropagation neural networks (BPNNs), long short-term memory (LSTM), and hybrid models, were analyzed. The results from the systematic review indicate a growing preference for hybrid models, which effectively capture hidden relationships between GWL and environmental factors. The root mean square error (RMSE) emerges as the predominant performance metric, highlighting its significance in evaluating NNs. Results from the review also highlight the significance of comprehensive, long-term datasets covering a decade for robust trend analyses and accurate predictions. The findings contribute to a deeper understanding of new trends in groundwater research such as the application of neural networks for prediction problems in groundwater research. In conclusion, a hybrid metaheuristic algorithm produced more efficient results emphasizing their efficacy. In addition, lagged values were essential input for GWL prediction. The paper addressed both technical nuances and broader environmental implications.

The people of Atebubu municipality depend almost entirely on groundwater for their daily water needs since they are not served with treated water from the Ghana Water Company Limited (GWCL). The municipality has been battling with perennial potable water crisis for the past few decades as supply from the groundwater sources is erratic. This paper used the electrical resistivity tomography (ERT) technique in the gradient and Wenner array configurations to access subsurface groundwater information leading to possible sustainable groundwater potential locations. The results show resistivity values ranging from 5 to 2212 Ωm across the profiles. Possible clay contents have resistivity distributions ranging from 5 to 98.6 Ωm and dominate the shallow subsurface and top layers in most cases. Distribution of the moderately low resistivity 100 to 506 Ωm also suggest that the water table for sustainable boreholes and wells could be found at deeper depths with only few portions stretching to the shallow subsurface. Rocks that are expected to exist at these depths are phyllites, sandstone, siltstone or arkose. The high to very high resistivity values which are interpreted as competent rock formations were observed predominantly at both deeper depth and the shallow subsurface.

The Owabi catchment which is about 69 km² provides about 20 % of water needs of the Kumasi metropolis has been in recent times prone to high anthropogenic activities that threaten water resource management. The Soil-Water-Assessment-Tool (SWAT) was used to assess the extent of these activities on the hydrology on the catchment from 1986 to 2015. Specifically, the model simulated historic and projected stream-flow and water balance. Initial results revealed the forest and topography played major role in water loss at the catchment as evapotranspiration and surface runoff were the dominant modulating processes. Monthly calibration/validation of the model yielded satisfactory results with NSE (0.66/0.67), R² (0.67/0.67), PBIAS (8.2 %/8.0 %) and RSR (0.59/0.58). Nine sensitive parameters of which the catchment slope (CN2) ranked principal were found to control runoff amounts into the river. The model uncertainty was also quite low as the 95PPU enveloped about 50 % of the observed streamflow within a width of 0.45–0.55. Furthermore, future streamflow predictions were modelled under RCP2.6, RCP4.5 and RCP8.5 climatic scenarios, and two landuse scenarios, landuse category 1 and 2 (LU1 and LU2). An increasing trend of the downscaled rainfall totals between 2021 to 2050 for all RCPs were observed. This will positively impact streamflow generation at the catchment under LU1. There is an expected deficit of streamflow amounts under LU2 relative to LU1, and a marginal reduction as compared to the baseline. In general, the model proved efficient in determining the hydrology parameters in the catchment and therefore has potential to be used for further modelling of water quality and pollution to aid effective water resource decisions at the catchment.

The focus of groundwater research has evolved, expanded, and adapted to meet the water demands of society. In recent years, discernible trends have emerged in groundwater studies, particularly in the domains of exploration and exploitation. Groundwater exploration in West Africa has predominantly been driven by demand and ease of accessing water in its hosting environment. In light of the increasing dependence and the difficulty of groundwater accessibility in some environments, numerous methods have been employed to facilitate the rapid assessment of groundwater resources. This review explores into the diverse methods employed in groundwater exploration, placing particular emphasis on analyzing the evolution from geophysical methods to the application of machine learning in West Africa. The PRISMA technique was employed for data collection, screening, and verification of data eligibility. Researchers associated with Nigerian institutions contributed to 57% of all articles, followed by researchers from Ghanaian institutions. The predominant method employed in groundwater exploration, as evidenced by the highest number of published papers (18), was the electrical resistivity method followed by electromagnetic, and magnetic methodologies. Other techniques; such as induced polarization and gravity were employed in conjunction with other geophysical methods to enhance and/or corroborate the obtained results. Our research findings revealed that geophysical techniques played a crucial role in delineating the diverse geological environments relevant to groundwater accumulation and movement. In addition, Geographic Information Systems (GIS) and remote sensing have been integrated with geophysical methods to investigate the spatial distribution of groundwater resources. While these geophysical methods have demonstrated effectiveness, their interpretation relies on anomalies, which can be influenced by various factors. Incorrect interpretations may arise without thorough background studies on the geology of the area. Despite the fact that global trends are increasingly favoring the adoption of machine learning in groundwater exploration with great success in delineating potential groundwater reserves, our research reveals a notable scarcity of such endeavours in West Africa. Groundwater exploration in West Africa primarily revolves around the aforementioned geophysical methods and Geographic Information System (GIS). The review underscores the urgency for support, both financially and in terms of technological expertise, to promote research involving machine learning in West African countries. A key finding is the significant underrepresentation of machine learning studies by West African researchers in groundwater exploration, despite its demonstrated cost-effectiveness, time efficiency, and enhanced reliability.