GeoHazards

Landslides are a major natural hazard capable of causing severe damage to infrastructure, ecosystems, and human life. They result from complex interactions of geological, hydrological, and environmental factors, with soil properties playing a crucial role by influencing the mechanical behavior and moisture dynamics of slope materials that drive initiation and progression. In South Africa, few studies have examined soil influences on landslide susceptibility, and none have been conducted in the Eastern Cape Province. This study investigated the role of soil physical and chemical properties in landslide susceptibility by comparing profiles from landslide scars and stable sites in the Port St. Johns and Lusikisiki region. Samples from topsoil and subsoil horizons were analyzed for soil organic matter (SOM), cation exchange capacity (CEC), saturated hydraulic conductivity (Ksat), exchangeable sodium adsorption ratio (SARexc), and texture. Statistical analyses included the Shapiro–Wilk test to evaluate data normality. For inter-profile comparisons, Welch’s t-test was applied to normally distributed data, while the Mann–Whitney U test was used for non-normal distributions. Intra-profile differences across more than two groups were assessed using the Kruskal–Wallis test for the non-normally distributed data. Results showed that landslide-prone soils had higher SOM, CEC, and Ksat in topsoil, promoting moisture retention and rapid infiltration, which favor pore pressure build-up and slope failure. Non-landslide soils displayed higher sodium-related indices and finer textures, suggesting more uniform water retention and resilience. Vertical variation in landslide soils indicated hydraulic discontinuities, fostering perched saturation zones. Findings highlight landslide initiation as a product of interactions between hydromechanical gradients and chemical dynamics.

Slope failures represent one of the most serious geotechnical hazards, which can have severe consequences for personnel, equipment, infrastructure, and other aspects of a mining operation. Deterministic and stochastic conventional methods of slope stability analysis are useful; however, some limitations in applicability may arise due to the inherent anisotropy of rock mass properties and rock mass interactions. In recent years, Machine Learning (ML) techniques have become powerful tools for improving prediction and risk assessment in slope stability analysis. This review provides a comprehensive overview of ML applications for analyzing slope stability and delves into the performance of each technique as well as the interrelationship between the geotechnical parameters of the rock mass. Supervised learning methods such as decision trees, support vector machines, random forests, gradient boosting, and neural networks have been applied by different authors to predict the safety factor and classify slopes. Unsupervised learning techniques such as clustering and Gaussian mixture models have also been applied to identify hidden patterns. The objective of this manuscript is to consolidate existing work by highlighting the advantages and limitations of different ML techniques, while identifying gaps that should be analyzed in future research.

Sediment erosion under turbulent wave action is a highly dynamic process shaped by the interaction between wave properties and sediment characteristics. Despite extensive empirical research, the underlying mechanisms of wave-induced erosion remain insufficiently understood, particularly regarding the threshold energy required for particle mobilization and the factors governing displacement patterns. This study employed a custom-built wave flume and a 3D-printed sampler to examine sediment behavior under controlled wave conditions. Rounded glass beads, chosen to eliminate the influence of particle shape, were used as sediment analogs with a similar specific gravity to natural sand. Ten experiments were conducted to systematically assess the effects of particle size, particle number, input voltage (wave power), and water depth on sediment response. The results revealed that (1) only a fraction of particles were mobilized, with the remainder forming stable interlocking structures; (2) the number of displaced particles increased with particle size, particle count, and water depth; (3) a threshold wave power is required to initiate erosion, though buoyancy under shallow conditions reduces this threshold; and (4) wave steepness, rather than voltage or wave height alone, provided the strongest predictor of sediment displacement. These findings highlight the central role of wave steepness in erosion modeling and call for its integration into predictive frameworks. The study concludes with methodological limitations and proposes future research directions, including expanded soil types, large-scale flume testing, and advanced flow field measurements.