Gully erosion susceptibility prediction in Mollisols using machine learning models

Abstract

In recent years, gully erosion has caused soil loss, land degradation, and a large sediment yield in the Mollisols in northeastern China, threatening agricultural development and national food security. Moreover, the prediction of gully erosion remains a great challenge owing to the difficulty of determining suitable environmental indicators and identifying the best models for predicting gully erosion prone areas. Therefore, the objective of this study was to quantify the contributions of the main factors controlling gully erosion and to identify the best model for predicting areas susceptible to gully erosion in Hailun City, northeastern China. Initially, the spatial distribution of the gully erosion was investigated through visual interpretation of GaoFen-1 satellite images. The analyzed gullies were evenly distributed in the study region, and we selected 70% of the gullies as the training data set and the remaining 30% as the validation data set. Subsequently, 12 variables, including the elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), soil type, land use, normalized difference vegetation index (NDVI), precipitation, distance from rivers, and distance from existing gullies, were selected as the indicators of gully erosion. Then, multicollinearity analysis was conducted to determine the main indicators without linearity. Finally, the contributions of the indicators and the areas susceptible to gully erosion were determined using machine learning models, including support vector machine (SVM), multilayer perceptron neural network (MLPNN), random forest (RF), and extreme gradient boosting (XGBoost) models. The results revealed that there was no multicollinearity among the 12 indicators, so they were all employed in the machine learning models for the gully erosion susceptibility prediction. The XGBoost model had the highest R2 and lowest root mean square error (RMSE) values in the model validation stage (0.81 and 0.60, respectively), followed by the RF (0.78 and 0.61, respectively), MLPNN (0.65 and 0.70, respectively), and SVM (0.62 and 0.70, respectively). The gully distance had the largest relative importance score (>35%) for gully erosion, followed by the profile curvature, plan curvature, land use, elevation, and soil type, which had relative importance scores of 10% to 15%. The gully erosion susceptibility map revealed that the central part of the study area was more susceptible to gully erosion than the other regions. These results can help managers to identify the regions that are prone to gully erosion and to design soil conservation practices to slow down the soil erosion process.