farokh asadzadeh

Prediction of soil wind erodibility through soil characteristics is an important aspect for modeling soil wind erosion. This study was conducted to compare the efficiency of multiple linear regression (MLR), artificial neural network (MLP), artificial neural network based on genetic algorithm (MLP-GA) and artificial neural network based on whale optimization algorithm (MLP-WOA) for prediction of soil wind erodibility in part of eastern land of Urmia Lake. In this research, 96 soil samples were collected based on a stratified random sampling method and their physicochemical properties were measured. Additionally, the wind erodibility of soil samples was measured using a wind tunnel. Among the 32 measured soil properties, four properties including the percentages of fine sand, size classes of 1.7-2.0, and 0.1-0.25 mm (secondary particles) and organic carbon were selected as the model inputs by stepwise regression. Result showed that the MLP-WOA was the most effective method for predicting soil wind erodibility in the study area regarding to the lowest RMSE (2.9) and ME (-0.11), and the highest R2 (0.87) and NSE (0.87) values; followed by MLP-GA, MLP, and MLR. Considering the high efficiency of MLP-WOA, This method can be used as a promising method for determination of soil wind erodibility in the study area.

Huge parts of Urmia Lake have been dried up in recent years. As a result, vast areas of salty fine sand dunes, which are very susceptible to wind erosion, have been formed. This study was conducted to investigate the performance of Particle Size Distribution (PSD) models in description of sand dune PSDs and predicting wind erodibility by using PSD models in parts of western coast of Urmia Lake. Totally, 48 samples from a grid of 500m × 500m were collected. PSD of the samples was determined by dry sieving method. Statistical parameters of grain size including mean, sorting, skewness and kurtosis were calculated. Performance of nine PSD models in describing the grain size distribution was assessed by the coefficient of determination (R2), Root mean square error (RSME) and Akaike criterion (AIC) of the models. To determine wind erodibility of sand dunes, mass fractions of aggregates.

Understanding the spatial distribution and variability of erodibility and soil properties is essential for planning of water conservation methods, controlling of flood and runoff and managing of soil erosion or watershed. Selecting and using appropriate interpolation techniques for soil properties and erodibility mapping by erosion models such as WEPP is essential. The objective of this study was regionalization of interrill erodibility and effective factors like clay, organic matter and lime using kriging and cokriging and remote sensing data (Landsat 7). For this purpose, 100 soil samples were selected randomly from 0-15 cm depth of Selin watershed in Kaleibar region of East Azerbaijan. Interrill erodibility of WEPP model and some soil properties as clay, organic matter and lime were measured. Correlation analysis between soil properties and digital number (DN) ETM image showed that clay, organic matter, lime and interrill erodibility had the highest correlation with DN of Band 7, 1, 1 and 3 ETM image (−0.406, -0.431, 0.291 and 0.299), respectively. Therefore, the DN of these bands used as auxiliary data for cokriging estimator. The spherical model was fitted the best model to calculate variogram of interrill erodibility, clay, organic matter and lime. No significant difference were noted between kriging and cokriging despite using remote sensing data as auxiliary data. This can be attributed no strong correlation between interrill erodibility, clay, organic matter and lime and remote sensing data.