The sedimentological, mineralogical, and petrographic compositions of the Albian - Lower Turonian deposits at four sites in the northern part of the Douala sub-basin located in the heart of the Guinea Gulf were investigated to determine their provenance, paleoclimate, and diagenetic effects from thirty-eight (38) samples. Several lithologies are identified, ranging from clays/shales to silty clays, poor consolidated fine-to coarse-grained sandstones, and conglomerates to conglomeratic sandstones. The deposits are made up of successive positive sequences in the eastern part of the Mungo River and cyclic deposits in the western part. The sandstones are primarily arkose wacke, secondarily arkose, subarkose, sublith-arenite, and rare lithic arenite and wacke derived from the Neoproterozoic basement formed during the Pan-African orogenesis. The presence of heavy minerals such as tourmaline, kyanite and sillimanite indicates that the deposits originate from plutonic and metamorphic sources. The rarity of these latest in the sediments suggest the intense chemical weathering that has affected the source rocks. These deposits are mainly proximal. Additionally, the association of polycrystalline quartz with undulatory extinction and monocrystalline quartz with non-undulatory extinction suggests a mixed source. The various microstructures and morphologies of zircon grains including prismatic, sub-angular and sub-prismatic shapes further support a mixed provenance. The dominance of prismatic shape and non-undulatory monocrystalline quartz grains indicates a predominance of plutonic sources. X-ray diffraction analysis revealed that kaolinite is the dominant clay mineral found in clayey facies, though its moderate to low quantity indicates a more humid climate during the Albian-Cenomanian and a less humid climate during the Cenomanian. Physical alteration processes have mainly affected the studied deposits.

Groundwater is a critical resource for sustaining agricultural, domestic, and ecological needs in the Upper Blue Nile Basin of Ethiopia, where rapid population growth and climate variability have intensified water stress. The Temecha River Catchment, part of this basin, faces recurrent droughts and declining groundwater levels, underscoring the need for effective groundwater management strategies. One promising approach is Managed Aquifer Recharge (MAR), whose success relies heavily on accurate groundwater level monitoring and forecasting. Although MAR decisions are influenced by hydrogeological and land use factors, hydraulic head data are essential for determining recharge timing and suitability. This study evaluates the performance of five machine learning models—Gradient Boosting Regression (GBR), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Linear Regression (LR)—for predicting groundwater level fluctuations in the Temecha River Catchment from 1995 to 2023. Rainfall, temperature, and evapotranspiration were used as predictive variables. Among the models, GBR showed superior performance, with a Mean Absolute Error (MAE) of 0.07 m, Nash-Sutcliffe efficiency (NSE) of 0.7934 m, Coefficient of Determination (R 2 ) of 0.7856, and Percent Bias (PBIAS) of −2.408 %. The results demonstrate GBR's effectiveness in groundwater level forecasting and support its application in data-scarce regions to inform sustainable water resource management. However, adaptation to other regions must consider local hydrogeological and climatic conditions. One sentence summary: This study explores the use of machine learning techniques in Python to predict temporal groundwater levels in the Temecha River Catchment.

Groundwater, a primary freshwater source for potable and agricultural uses, is increasingly threatened in southern coastal areas of Bangladesh because of rapid urbanization, industrialization, and climate change. Bagerhat, one of the coastal districts, faces not only frequent tropical cyclones, inundation of low-lying areas, and saline water intrusion but also rapid urbanization, population growth, and pollution from prawn culture and seafood processing industries. The study aims to assess the hydrogeochemistry and the suitability of groundwater based on its significance for potable and irrigation purposes in Bagerhat District. This research includes an integrated approach of hydrogeochemical, geospatial, and multivariate statistical methods to investigate the hydrogeochemical processes and groundwater quality using ninety groundwater samples from existing deep tube wells (around 350 m depth). The hydrogeochemical analysis determined the concentrations of major cations and anions were Na+>Mg2+> Ca2+> K+ and Cl− > HCO3- > SO42− > NO3-, correspondingly. Electrical Conductivity (EC) values ranged from 16,260 μS/cm in the southern Mongla Upazila to a minimum of 560 μS/cm in Chitalmari Upazila. The evaporation–crystallization processes were found to be the primary mechanism influencing groundwater chemistry in the study area. Multivariate statistics, including Pearson correlation matrix, principal component analysis (PCA), and cluster analysis, indicated that geogenic processes govern groundwater chemistry. The spatial distribution of the water quality index (WQI) map demonstrated that only 15% of groundwater samples are suitable for drinking in the central and eastern parts, while 85% are unsuitable in the study area. Furthermore, evaluation of irrigation water quality parameters, such as sodium percentage (Na%), sodium adsorption ratio (SAR), Kelly ratio (KR), permeability index (PI), magnesium hazard (MH), and salinity hazard, indicated that most wells are unsuitable for irrigation. The results provide insights into aquifer hydrogeochemistry, groundwater quality status, vulnerable areas of pollution, and sustainable, safe water options for groundwater management in the Bagerhat district.