Gully erosion susceptibility assessment based on machine learning-A case study of watersheds in Tuquan County in the black soil region of Northeast China

Abstract

Owing to its unique topographic and climatic conditions, agricultural production, and anthropogenic grazing, severe gully erosion has developed in the watersheds of Tuquan County located in the black soil region of Northeast China. Using three machine learning models to assess gully erosion susceptibility in this region, the main purpose of this study was to determine the important factors affecting the occurrence of gully erosion from 25 geo-environmental factors and select the model with the best prediction performance. Field data collection was conducted for 823 gullies. 12,496 pixels corresponding to 823 gullies and 12,496 pixels without gully erosion were randomly selected to form the model calculation database. According to the principle of multiple screening, this study selected 25 geo-environmental factors affecting the occurrence of gully erosion. Two factors, coarse sand content and fine sand content, were excluded by multi-collinearity analysis and using random forest (RF), convolution neural network (CNN), and transformer models, combining the 10-fold cross-validation and related validation metrics to assess the susceptibility of gully erosion in Tuquan County’s small watersheds. The results show that the convergence index (CI), topographical wetness index (TWI), terrain ruggedness index (TRI), and distance from the river based on the RF model are important factors affecting the occurrence of gully erosion. The transformer model (mean AUC of training = 95.338 %, mean AUC of validation = 95.342 %) was superior to the RF (mean AUC of training = 99.997 %, mean AUC of validation = 92.518 %) and CNN (mean AUC of training = 86.995 %, mean AUC of validation = 84.647 %) models in predicting performance. To ensure that the management and decision-making departments take reasonable and effective measures to prevent gully erosion in the study area, the gully erosion susceptibility maps produced using the three machine learning models were beneficial.