Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method

Abstract

The source region of the Yellow River (SRYR), known as the “Chinese Water Tower”, is currently grappling with severe soil erosion, which jeopardizes the sustainability of its alpine grasslands. Large-scale soil erosion monitoring poses a significant challenge, complicating global efforts to study soil erosion and land cover changes. Moreover, conventional methods for assessing soil erosion do not adequately address the variety of erosion types present in the SRYR. Given these challenges, the objectives of this study were to develop a suitable assessment and prediction model for soil erosion tailored to the SRYR’s needs. By leveraging soil erosion data measured by 137Cs from 521 locations and employing the random forest (RF) algorithm, a new soil erosion model was formulated. Key findings include that: (1) The RF soil erosion model significantly outperformed the revised universal soil loss equation (RUSLE) model and revised wind erosion equation (RWEQ) model, achieving an R2 of 0.52 and an RMSE of 5.88. (2) The RF model indicated that from 2001 to 2020, the SRYR experienced an average annual soil erosion modulus (SEM) of 19.32 t·ha−1·y−1 with an annual total erosion in the SRYR of 225.18 × 106 t·y−1. Spatial analysis revealed that 78.64% of the region suffered low erosion, with erosion intensity declining from northwest to southeast. (3) The annual SEM in the SRYR demonstrated a downward trend from 2001 to 2020, with 83.43% of the study area showing improvement. Based on these findings, measures for soil erosion prevention and control in the SRYR were proposed. Future studies should refine the temporal analysis to better understand the influence of extreme climate events on soil erosion, while leveraging high-resolution data to enhance model accuracy. Insights into the drivers of soil erosion in the SRYR will support more effective policy development.