Land conversion to cropland is one of the major causes of severe soil erosion in Africa. This study assesses the current cropland extent and the related soil erosion risk in Rwanda, a country that experienced the most rapid population growth and cropland expansion in Africa over the last decade. The land cover land use (LCLU) map of Rwanda in 2015 was developed using Landsat-8 imagery. Based on the obtained LCLU map and the spatial datasets of precipitation, soil properties and elevation, the soil erosion rate of Rwanda was assessed at 30-m spatial resolution, using the Revised Universal Soil Loss Equation (RUSLE) model. According to the results, the mean soil erosion rate was 250 t·ha⁻¹·a⁻¹ over the entire country, with a total soil loss rate of approximately 595 million tons per year. The mean soil erosion rate over cropland, which occupied 56% of the national land area, was estimated at 421 t·ha⁻¹·a⁻¹ and was responsible for about 95% of the national soil loss. About 24% of the croplands in Rwanda had a soil erosion rate larger than 300 t·ha⁻¹·a⁻¹, indicating their unsuitability for cultivation. With a mean soil erosion rate of 1642 t·ha⁻¹·a⁻¹, these unsuitable croplands were responsible for 90% of the national soil loss. Most of the unsuitable croplands are distributed in the Congo Nile Ridge, Volcanic Range mountain areas in the west and the Buberuka highlands in the north, regions characterized by steep slopes (>30%) and strong rainfall. Soil conservation practices, such as the terracing cultivation method, are paramount to preserve the soil. According to our assessment, terracing alone could reduce the mean cropland soil erosion rate and the national soil loss by 79% and 75%, respectively. After terracing, only a small proportion of 7.6% of the current croplands would still be exposed to extreme soil erosion with a rate >300 t·ha⁻¹·a⁻¹. These irremediable cropland areas should be returned to mountain forest to foster environmental sustainability or further sustainable alternative erosion control techniques may be applied, such as applying Vetiver Eco-engineering Technology due to its economical soil erosion control and stabilization of steep slopes and the construction of erosion control dams to absorb and break down excess runoff from unusually intense storms in various parts of the watersheds.

This research assessed the soil erosion threat in the Congo Nile Ridge Region of Rwanda. The study forecasted erosion by applying the Revised Universal Soil Erosion (RUSLE) with five factors (rainfall, soil, topography, cover management, and support practices) and spatial data. About 85.5% of the area under investigation was predisposed to erosion with unsustainable average soil loss rates of > 1 t/ha/yr. The outcomes of the research highlighted that the average rate of estimated soil loss in the region prone to erosion was > 63.62 t/ha/yr, resulting in an overall annual predicted soil loss of approximately 44 × 10⁶ t in 2016. All of the districts studied have steep slope gradients (30.4%–36.1%) and high annual rainfall totals (1199–1484 mm/yr), except Rubavu district. More than 88.8% of croplands had unsustainable average soil loss rates of > 1 t/ha/yr. The analysis indicated that both terracing and strip cropping have the potential to reduce rates of soil loss in the farmland, by 64.4% and 10.4%, respectively. The results of this study will serve as a baseline for soil erosion mitigation and land-use planning in the study area and Rwanda at large.

Wetland maps are a prerequisite for wetland development planning, protection, and restoration. The present study aimed at mapping and monitoring Rwanda’s Akagera Complex Wetland by means of remote sensing and geographic information systems (GIS). Landsat data, spanning from 1987 to 2015, were acquired from different sensor instruments, considering a 5-year interval during the dry season and the shuttle radar topographic mission (SRTM) digital elevation model (30-m resolution) was used to delineate the wetland. The mapping and delineation results showed that the wetland narrowly extends along the Rwanda-Tanzania border from north to south, following the course of Akagera River and the total area can be estimated at 100,229.76 ha. After waterbodies that occupy 30% of the wetland’s surface area, hippo grass and Cyperus papyrus are also predominant, representing 29.8% and 29%, respectively. Floodplain and swamp forest have also been inventoried in smaller proportions. While the wetland extent has apparently remained stable, the inhabiting waterbodies have been subject to enormous instability due to invasive species. Lakes, such as Mihindi, Ihema, Hago and Kivumba have been shrinking in extent, while Lake Rwanyakizinga has experienced a certain degree of expansion. This study represents a consistent decision support tool for Akagera wetland management in Rwanda.

This paper aimed to investigate the influence of climatic and topographic factors on the distribution of vegetation in the Virunga Volcanoes Massif using GIS and remote sensing techniques. The climatic variables considered were precipitation, Land Surface Temperature (LST), and evapotranspiration (ET), whereas the topographic factors considered were elevation and aspect. The dataset consisted of MODIS NDVI data, satellite-delivered precipitation, ET, and the LST. A 2014 Landsat 8 OLI image was used to produce a vegetation map of the study area, while DEM was used to derive the elevation attributes and to calculate the aspect angles. Moran’s I and Geographically Weighted Regression (GWR) Model was used to analyze the relationships between the climatic factors and NDVI changes over elevation and aspect. The results indicated that among the nine vegetation types inventoried in the area, the Mean NDVI varied from 0.33 to 0.59 and the optimal vegetation growth was found at an elevation between 2000 and 3900 m, with mean NDVI values larger than 0.50. The peak mean NDVI value of 0.59 was found at the elevation from 2100 to 2800 m. Vegetation growth was found to be more sensitive to elevation, as NDVI values were more varied at a lower elevation (4000 m). Considering the aspect, the greater vegetation growth was found in SE (132°, 148°), SW (182°, 186°), and NW (309.5°–337.5°), with mean NDVI values larger than 0.56. This indicated that vegetation was susceptible to better growth conditions in the lower elevation ranges and in shady areas. The vegetation NDVI in this study area was mostly uncorrelated with precipitation (R² = 0.34), but was strongly correlated with LST (R² = 0.99) and ET (R² = 98). LST (≥18 °C) and ET (1286 mm/year⁻¹) were found to provide optimal conditions for vegetation growth in the Virunga Volcanoes Massif. Empirically, the results concluded that elevation, aspect, LST, and ET are the main factors controlling the spatial distribution and vegetation growth in this area. This information is significantly helpful for biodiversity conservation and constitutes a valuable input to environmental and ecological research.

Student dropout in higher education remains a global challenge, particularly in developing regions where early interventions are hindered by reliance on traditional indicators like GPA or attendance. This study addresses the issue by proposing a predictive model using ensemble machine learning techniques, integrating Logistic Regression, Random Forest, and AdaBoost. These models were combined using soft and hard voting classifiers to enhance prediction accuracy and reliability. The dataset, comprising 4,424 student records, includes demographic, academic, and socio-economic features. Results showed that the soft voting ensemble achieved the highest accuracy (80.56%) and AUC (91%), outperforming individual classifiers. Feature importance analysis revealed academic performance, tuition status, and parental background as key predictors of dropout. The model not only identifies at-risk students with high precision but also offers actionable insights for early intervention. This approach equips higher learning institutions with data-driven strategies to improve retention and student success outcomes.

This study, "A Comparative Analysis of Ensemble-Based Models for Predicting Cryptocurrency Price Movements," evaluates ensemble machine learning models bagging, boosting, and stacking to improve cryptocurrency price prediction accuracy. Using historical data, models like Random Forest, Gradient Boosting, and Stacking were tested, with Stacking emerging as the top performer (81.80% accuracy, 81.49% F1-score, 88.43% AUC-ROC), outperforming traditional methods like Naive Bayes and Decision Trees. The Boosting Combined model also showed strong results. The research highlights the effectiveness of ensemble techniques in handling cryptocurrency market volatility, offering valuable insights for traders and investors. It underscores the potential of advanced feature engineering and real-time testing to further enhance predictive accuracy, advancing financial decision-making and risk management in the cryptocurrency sector.