Optimal feature selection for prediction of wind erosion threshold friction velocity using a modified evolution algorithm

Threshold friction velocity (u(*t)) is a very important parameter, which represents wind erosion potential. Because of the difficulty of measuring u(*t), it would be advantageous if u(*t) could be estimated indirectly from its effecting factors that can be easily measured. The main purpose of this research was to quantify relationships between u(*t) and various topsoil features using inexpensive approaches. To prepare a reliable dataset, we used a portable wind tunnel for measuring u(*t) at a total of 118 observation points in Kerman province, southeast Iran. We developed a non-dominated sorting genetic algorithm II (NSGA-II), specifically designed to operate with artificial neural network (ANN) to select the most determinant properties that influence u(*t). A permutation of nine input features including surface crust (SC), gravel coverage (GC), very fine sand (VFS), fine sand (FS), very coarse sand (VCS), electrical conductivity (EC), sodium adsorption ratio (SAR), calcium carbonate equivalent (CCE), and mean weight diameter (MWD), was introduced as explanatory variables. We also examined the potential power of using a Multi-Layer Perception (MLP) neural network for prediction of u(*t) changes in response to spatial variation of the selected features. The results of constructed MLP model revealed the ability of the model for u(*t) prediction and showed that the coefficient of determination (R-2) values were 0.91 and 0.89 for training and testing data, respectively. Furthermore, acceptable level of the statistical validation criteria verified reliable performance of the MLP model. This research provided a powerful basis for prediction of u(*t) from topsoil features and surface roughness in arid and semi-arid areas of Iran; however, its generic framework could be used to other arid and semi-arid regions with similar challenges.