Prediction of soil wind erodibility using a hybrid Genetic algorithm – Artificial neural network method

Abstract

Understanding and consequent modeling of soil wind erodibility is hampered by the complex nature of the eroding processes and limited empirical data. It is often necessary to resort to robust approaches capable of finding correlated patterns among soil erodibility magnitudes and their drivers. To signify soil erodibility to wind, we used a portable wind tunnel to measure wind erosion rate (g m−2 s−1) at a total of 118 sites in Kerman Province, southeast Iran. At each sampling site, 17 different factors affecting soil erodibility were measured. Gravel coverage, surface crust, very fine and very coarse sands, aggregate stability, and calcium carbonate equivalent (CCE) were introduced as the more important parameters affecting soil erodibility by hybrid Genetic Algorithm-Artificial Neural Network (GA-ANN). A Multi-Layer Perception (MLP) neural network was developed to predict erodibility changes in response to spatial variation of the selected features. The developed MLP-model provided a strong basis for the prediction of soil erodibility, where the coefficient of determination (R2) values of 0.89 and 0.87 were obtained by comparing the measured and predicted wind erosion rates for the training and testing data, respectively. The acceptable levels of the statistical validation criteria were also an indication of the proper performance of the model. Furthermore, the soil erodibility was sensitive respectively to surface crust, very fine sand, and very coarse sand parameters.