Simulation and Analysis of Hydraulic Erosion in Sloping Farmland Based on Gradient Boosting Decition Tree Mode

Abstract

[Objective] This article employs machine learning to quantitatively analyze soil water erosion in Loess Plateau slope farmland, addressing its complexity and quantification challenges due to human interference. We aim to simulate erosion characteristics, explore its mechanisms, and support erosion prediction. [Methods] Using 1959-1969 data from Zizhou Experimental Station, we characterized the influencing factors and analyzed erosion and runoff depth changes with a gradient boosting decision tree. [Results] The dataset showed significant variability in secondary rainfall erosion (0~122.72 t/km2), runoff depth (0.02~17.20 mm), rainfall duration (2~1 410 min), and average intensity (0.02~4.63 mm), often right-skewed. The erosion model (R2=0.81) slightly outperformed the runoff depth model (R2=0.80), despite its greater complexity (8 layers vs. 5). Using the gradient boosting tree model and SHAP algorithm, we found differing key factors for erosion and runoff. [Conclusion] Limitations in feature extraction lead to less accurate predictions for small erosion and runoff depths. Future research should explore more independent variable combinations to enhance predictions. Main influencing factors differ between runoff and sediment production. Precipitation mainly influences runoff, while erosion and sediment production depend on precipitation and terrain-related variables. In summary, this data-driven study illuminates slope farmland erosion mechanisms on the Loess Plateau, providing a scientific basis for erosion control in the region.

Keywords

• prediction model
• gradient lifting tree model
• sloping farmland
• loess slope surface