چكيده لاتين
Soil is one of the most important and valuable natural resources, providing around 95% of human food. Without healthy soil, life on Earth would not be possible. Soil erosion is a physical process and one of the most dangerous mech-anisms of land degradation that leads to the reduction of fertility and damage to agricultural lands. One of the major processes of soil degradation is gully ero-sion, which causes significant soil loss and produces large amounts of sediment in various climates. Gully erosion is considered one of the most complex and destructive types of water erosion. Therefore, studying, identifying, and mapping areas prone to gully erosion has become highly necessary. The aim of this study is to identify the factors influencing gully erosion occurrence, predict, and map the likelihood of its occurrence in the Mianab-Shushtar watershed. Initially, us-ing Google Earth images, satellite imagery and field surveys, a map of gully dis-tribution points was prepared. In this research, 4000 points with the presence of gullies and another 4000 points without gullies were used. Next, the most im-portant topographic indices were prepared as environmental parameters influ-encing gully erosion occurrence. The independent variables affecting gully ero-sion include elevation, slope degree, slope direction, slope length index (LS), topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), lithology, land use, five vegetation indices including NDVI, EVI, ARVI, SAVI and OSAVI, distance to streams, stream density, distance to faults, fault density, distance to roads, road density, average annual precipitation, rain-fall erosivity index (R), soil texture (sand, clay and silt), and soil erodibility in-dex (K).Among the 14 machine learning algorithms implemented in this study, CatBoost, LightGBM, and AdaBoost algorithms showed higher accuracy with an F1-score over 94% compared to other algorithms. Additionally, among the 23 indices influencing gully erosion occurrence in the area, land use was identified as the most important variable. The next most influential indices were ARVI and stream density. Based on the classification results of the three best algorithms, about 67/984% of the area falls in the very low-risk class, 14/074% in the low-risk class, 7/739% in the moderate-risk class, 5/363% in the high-risk class, and 4/837% in the very high-risk class. The results of this study will play a signifi-cant role in preventing and reducing the damage caused by gully erosion in the studied region.
