Gully Erosion Risk Assessment Using a GIS-Based Bivariate Statistical Models and Machine Learning in the Dodota Alem Watershed, Ethiopia

One of the most significant environmental hazards threatening ecosystems is gully erosion. In this study, we applied two bivariate statistical models—frequency ratio (FR) and index of entropy (IoE)—as well as a machine learning algorithm (RF) to generate gully erosion susceptibility maps (GESM). The study was conducted in the Dodota Alem watershed of the Awash River basin, covering 135 km². Our modeling utilized input data from field surveys, Google Earth, and secondary sources. Geo-environmental factors such as land use and land cover, soil characteristics, altitude, slope, aspect, profile curvature, plan curvature, drainage density, distance from roads, distance from streams, stream power index (SPI), and topographic wetness index (TWI) were considered after a multi-collinearity test. Among these factors, distance from roads had the most substantial impact on gully erosion susceptibility according to the RF model, while SPI played a crucial role in the FR and IoE models. Approximately 60% of the watershed falls into the moderate or high susceptibility category for gully erosion using the FR and IoE models, whereas the RF model projected the largest area in the very high susceptibility class. Validation results, based on the Area Under Curve (AUC), demonstrated prediction efficiencies of 0.912 (FR), 0.880 (IoE), and 0.932 (RF). These findings can guide decision-makers and planners in implementing effective soil and water conservation measures to mitigate the damage caused by gully erosion. Additionally, this approach serves as a valuable reference for future research on gully erosion susceptibility.