Machine learning applications for water-induced soil erosion modeling and mapping

•Erosion pins were used to measure annual soil erosion rate.•Annual soil erosion rates were modeled using three machine learning techniques.•Boosted regression trees yielded the most favorable results for soil erosion modeling.•The slope degree was the most important factor affecting soil erosion. Assessment of water-induced soil erosion as a crucial part of soil conservation plans is costly and time-consuming when applied to an extensive area. In this study, we propose a methodology based on recording the annual soil erosion in a portion of the study area using erosion pins and assessing the spatial distribution of soil erosion for the entire area using machine learning techniques. First, soil erosion pins were installed, and the amount of soil loss in each pin was recorded. The controlling factors of soil erosion (percentage of vegetation canopy, curvature, slope degree, slope length, percentage of sand, percentage of silt, and percentage of clay) were determined, and the dataset was divided into training (75% of the data) and testing (25% of the data) subsets. Three machine learning algorithms, namely boosted regression trees (BRT), deep learning (DL), and multiple linear regression (MLR), were employed to identify the relationship between soil erosion and its controlling factors. Then, the methods were evaluated by comparison between the predicted and observed values on the testing subset using statistical coefficients including coefficient of determination (R-squared), normalized root mean squared error (NRMSE), and Nash-Sutcliffe efficiency (NSE). Results show that the BRT outperformed the other algorithms in the assessment of the annual soil erosion (R-squared: 0.92, NSE: 0.9, and NRMSE: 0.32). Finally, the optimal algorithm (BRT) was selected to estimate the spatial distribution of soil erosion across the entire study area, and the final erosion map was verified using additional verification pins.