Megersa Olumana Dinka

Flood prediction is a critical tool for disaster management and risk mitigation. Machine learning models are viable alternatives to traditional techniques of flood prediction and analysis, which often fail to capture complex nonlinear relationships among meteorological parameters. This study evaluated the performance of an artificial neural network (ANN) to predict a flooding indicator (surface volume) in the Vaal River Basin using historical rainfall, wind speed, humidity, and maximum temperature data. A 30-year dataset (1994–2024) from the South African Weather Service was preprocessed and used to train and validate multiple ANN architectures. Model performance was assessed using RMSE, MAPE, MAE, VAE, and R-value metrics. The ANN with tansig–tansig activation functions and the Levenberg–Marquardt training algorithm achieved the best performance, demonstrating the viability of machine learning-based flood prediction for flood risk management.

This study presents a comprehensive investigation of the electronic, magnetic, and structural properties of platinum (Pt)-doped zinc oxide (ZnO) using first-principles calculations based on density functional theory (DFT) and the DFT+U approach. The inclusion of on-site Coulomb interactions via DFT+U offers a more accurate description of the localized Zn 3d orbitals, effectively capturing electron correlation effects. Pt substitution is found to enhance the thermodynamic stability of the ZnO lattice, as evidenced by negative formation energies and energetically favorable defect configurations. The electronic density of states at the Fermi level, N(EF), was evaluated for various magnetic configurations. In the non-magnetic state, N(EF) values of 6.36 and 2.17 states/eV were obtained for 12.5% and 50% Pt-doping, respectively, indicating a suppression of electronic states near the Fermi level with increasing Pt content. In the ferromagnetic configuration, this value decreased further to 0.392 states/eV, highlighting the influence of spin polarization. Antiferromagnetic calculations yielded a lower N(EF) of 0.2945 states/eV at 25% doping, suggesting a tendency toward enhanced metallicity under magnetic ordering. Band structure calculations revealed that Pt doping enables effective bandgap modulation. While pristine ZnO exhibited a bandgap of 1.80 eV within standard DFT, the DFT+U method increased this to 2.81 eV, in closer agreement with the experimental value of 3.34 eV. Density of states (DOS) analysis confirmed that Zn states dominate the conduction band near the Fermi level, while the oxygen contribution remains minimal. The phonon dispersion relations and phonon DOS for pristine ZnO and 50% Pt-doped ZnO reveal the dynamical stability and modifications in vibrational modes induced by Pt substitution. Furthermore, the introduction of Pt induces local magnetic moments, indicating the possibility of magnetic ordering in the host matrix. These findings underscore the potential of Pt-doped ZnO as a multifunctional material with tunable electronic and magnetic properties, suitable for future applications in spintronics and advanced semiconductor technologies.

Comprehension of spatio-temporal groundwater recharge (GWR) under climate change is imperative to enhance water resources availability and management. The main aim of this study is to examine climate change’s effects on spatio-temporal GWR. This study was done by ensembling five climate models and the physically-based WetSpass-M model to estimate GWR during baseline (1986 to 2015), mid-term (2031 to 2060), and long-term (2071 to 2100) periods for the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. In comparison to the Identification of unit Hydrographs and Component flows from Rainfall, Evaporation, and Streamflow (IHACRES)’s baseflow and direct runoff with corresponding WetSpass-M model outputs, the statistical indices showed good performance in simulating water balance components. Projected future temperature and rainfall will likely increase dramatically compared to the baseline period for RCP4.5 and RCP8.5. In comparison to the baseline period, the annual GWR had been projected to increase by 4.28 mm for RCP4.5 for the mid-term (MidT4.5), 15.27 mm for the long-term (LongT4.5), 2.38 mm for the mid-term (MidT8.5), and 13.11 mm for the long-term for RCP8.5 (LongT8.5), respectively. The seasonal GWR findings showed an increasing pattern during winter and spring, whereas it declined in autumn and summer. The mean monthly GWR for MidT4.5, LongT4.5, MidT8.5, and LongT8.5 will increase by 0.34, 1.26, 0.18, and 1.07 mm, respectively. The watershed’s downstream areas were receiving the lowest amount of GWR, and prone to drought. Therefore, this study advocates and recommends that stakeholders participate intensively in developing and implementing climate change resilience initiatives and water resources management strategies to offset the detrimental effects in the downstream areas.

Zinc oxide (ZnO), a wide-bandgap semiconductor, has garnered significant interest for photocatalytic applications due to its excellent chemical stability, non-toxicity, and strong oxidative capability. In this study, density functional theory (DFT) calculations were employed to explore the impact of ruthenium (Ru) doping on the structural, electronic, and magnetic properties of wurtzite ZnO. The introduction of Ru leads to bandgap narrowing and the emergence of impurity states, thereby enhancing visible light absorption. Charge density analysis reveals enhanced electron delocalization, while the projected density of states (PDOS) indicates strong hybridization between the Ru 4d orbitals and the ZnO electronic states. The density of states at the Fermi level, N(EF), exhibits a notable dependence on doping concentration and magnetic configuration. For non-magnetic states, N(EF) reaches 11 states/eV and 9.5 states/eV at 12.5% and 25% Ru concentrations, respectively. In ferromagnetic configurations, these values decrease to 0.65 states/eV and 1.955 states/eV, while antiferromagnetic states yield 4.945 states/eV and 0.65 states/eV. These variations highlight Ru’s crucial role in regulating electronic density, thereby affecting electrical conductivity, magnetic properties, and photocatalytic efficiency. The results offer theoretical guidance for designing high-performance Ru-doped ZnO photocatalysts.

Climate change affects streamflow through changes in precipitation, temperature, and extreme weather events. These changes will impact water resource availability significantly. Thus, understanding the impacts of climate change on hydrology is essential for sustainable water management. This study investigated the potential effects of climate change on streamflow in the Olifants River basin under shared socioeconomic pathways (SSPs), utilizing the restructured version of the Soil and Water Assessment Tool (SWAT+) model. Projected precipitation and temperature (Tmax and Tmin) were analyzed for the near (2030–2060) and far (2070–2100) future to simulate and analyze streamflow variations under SSP245 and SSP585 scenarios using bias-corrected CMIP6 data and the SWAT+ model. The SWAT+ model was calibrated and validated successfully, with Nash–Sutcliffe efficiency (NSE) values of 0.76 and 0.77, and coefficient of determination (R²) values of 0.78 and 0.82 during the calibration and validation periods, respectively. Climate model ensemble projections show a consistent decline in precipitation and increases in Tmax and Tmin, with Tmin increasing more significantly. These changes are projected to reduce streamflow, with annual declines of 43.08% and 50.89% under SSP245 and 57.79% and 58.82% under SSP585 for the near and far future, respectively. Moreover, climate change reduces streamflow across all seasons in the Olifants River basin. Therefore, adopting water management strategies such as enhancing integrated water resource management and investing in climate-resilient infrastructure is essential for sustainable water resource management under changing climate conditions in the basin.

Chromium, a prevalent heavy metal pollutant originating from various industrial processes, poses significant risks if not properly treated. In this study, batch adsorption of hexavalent chromium (Cr ⁶⁺ ) from water was performed using activated carbon derived from Cordia africana . This application represents a novel approach, as the use of this material for Cr ⁶⁺ removal has not been previously explored. The adsorbent was analyzed through Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), and the Brunauer–Emmett–Teller (BET), and Fourier Transform Infrared (FTIR) methods, revealing its structural and surface properties. Adsorption kinetics followed the pseudo-first-order model (R² = 0.99), while the Koble Corrigan isotherm (R² = 0.98) most accurately expressed the adsorption behavior. The adsorption experiments achieved a maximum Cr⁶⁺ adsorption capacity of 52.2 mg/g. Thermodynamic studies indicated that the process is spontaneous and endothermic. Reusability tests demonstrated the stability of the adsorbent over five cycles, with removal efficiencies ranging from 99.7 to 75.1%. Overall, Cordia africana leaf-based activated carbon exhibited a promising adsorption capacity, making it a cost-effective and readily available adsorbent suitable for industrial wastewater treatment.

Phenol and its derivatives in water and wastewater are highly toxic and challenging to degrade, posing serious environmental and health risks. Therefore, this research focuses on the removal of phenol from aqueous solutions using activated carbon made from Catha edulis stems. The activation process involved impregnating the Catha edulis stems with phosphoric acid followed by thermal treatment at 500 °C for 2 h. The resulting adsorbent was extensively characterized using various techniques, including Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), Brunauer–Emmett–Teller (BET) surface area analysis, and proximate analysis. Batch adsorption experiments were designed using a full factorial approach with four factors at two levels, resulting in 16 different experimental conditions. The characterization results showed that the activated carbon has a high surface area of 1323 m²/g, a porous and heterogeneous structure, and an amorphous surface with multiple functional groups. Under optimal conditions of pH 2, a contact time of 60 min, an adsorbent dosage of 0.1 g/100 mL, and an initial phenol concentration of 100 mg/L, the adsorbent achieved a phenol removal efficiency of 99.9%. Isotherm and kinetics analyses revealed that phenol adsorption fits the Langmuir model and pseudo-second-order kinetics, indicating a uniform interaction and chemisorptive process. This study highlights the effectiveness of Catha edulis stem-based activated carbon as a promising material for phenol removal in water treatment applications.

Zinc oxide (ZnO), a wide bandgap semiconductor, has attracted considerable 1 attention in photocatalysis due to its high chemical stability, non-toxicity, and strong 2 oxidation ability. In this study, density functional theory (DFT) calculations were employed 3 to investigate the effects of Ru doping on the structural, electronic, and magnetic properties 4 of wurtzite ZnO. The results reveal that Ru incorporation induces bandgap narrowing 5 and generates impurity states, enhancing visible-light absorption. Charge density analysis 6 suggests improved electron mobility, while projected density of states (PDOS) indicates 7 strong hybridization between Ru-4d and ZnO states. The calculated density of states at the 8 Fermi level, N(EF), exhibits notable dependence on doping concentration and magnetic 9 ordering. For non-magnetic states, N(EF) reaches 11 states/ eV and 9.5 states/eV at 12.5% 10 and 25% doping, respectively. In ferromagnetic configurations, these values reduce to 11 0.65 states/eV and 1.955 states/eV, while antiferromagnetic states yield 4.945 states/eV 12 and 0.65 states/eV. These variations highlight Ru’s role in modulating electronic density, 13 influencing conductivity, magnetic response, and photocatalytic potential. The findings 14 offer theoretical insights for designing efficient Ru-doped ZnO-based photocatalysts.