Prediction of Wind Erosion Threshold Velocity Using Portable Wind Tunnel Combined with Machine Learning Algorithms

Wind erosion is a key process in land degradation worldwide, especially in arid and semi-arid regions of Iran. This phenomenon is affected by many soil characteristics. The main objective of this study was to estimate the wind erosion threshold velocity using easily measurable soil characteristics along with data mining methods. For this purpose, wind erosion threshold velocity was measured in 100 areas in Fars province using a portable wind tunnel. Wind erosion threshold velocity was predicted by a support vector regression algorithm using easily measurable soil properties. In this regard, a genetic algorithm was used in order to obtain a set of parameters effective in estimating wind erosion threshold velocity. The results showed that the characteristics of soil moisture (r = 0.77), the size distribution of soil particles including the mean weight diameter of aggregate (r = 0.87) and the wind-erodible fraction of soils (r = -0.81), penetration resistance (r = 0.75), and organic matter (r = 0.33) have a high and significant correlation with wind erosion threshold velocity and play a key role in determining the threshold velocity of wind erosion in the region. According to the evaluation criteria, the combined support vector regression model with the genetic algorithm had the best performance and the most accurate estimate for wind erosion threshold velocity (RMSE = 0.53 and R2 = 0.92) and can be a promising method for estimation of wind erosion threshold velocity.