فهرست مطالب iraj kouchami sardoo

Wind erosion is an important cause of land degradation and desertification in arid and semi-arid regions of the world. This phenomenon occurs more severely in dry and bare soils. During wind erosion, soil particles are transported by three mechanisms known as creeping, saltation and suspension, which result in numerous on- and off-site damages. Wind erosion as a natural phenomenon, is affected by many factors. Our understanding of wind erosion is commonly constrained by the multiplicity and complexity of factors in this process. In recent years, solving pattern recognition and optimization problems with metaheuristic algorithms has received considerable attention among researchers. Genetic algorithms is a search technique inspired by the process of natural selection, which is well applied to multimodal, non-linear, and non-derivable objective functions. In the current research, for identifying the most important parameters affecting wind erosion rate, two GA-ANN and NSGA-II hybrid algorithms were developed using genetic algorithm and artificial neural networks. In order to prepare a suitable and reliable data set; after designing a grid sampling strategy, soil samples were collected from 51 study sites in the Narmashir plain, Kerman and then some soil parameters were measured. In addition, wind erosion rate was determined at each study site using a portable wind tunnel device. Based on the GA-ANN algorithm results, gravel coverage, sand, clay, aggregate stability, surface crust, moisture, and organic matter were identified as the main determinant features affecting spatial variation of wind erosion rate. However, the selected feature subset by NSGA-II algorithm included gravel coverage, sand, aggregate stability, surface crust, and moisture. The calculated error function for the GA-ANN algorithm performance was 3.58%. It was 1.70% for the NSGA-II algorithm performance. According to the results, both algorithms had acceptable performance to achieve the purpose of the present study. Therefore, the algorithms developed in this study can be applied to identify the most important parameters affecting wind erosion rate in other areas with similar challenges.

Aggregate stability of soils informs about their relative strengths against erosive forces and mechanical disruption. In this research, a hybrid Genetic Algorithm-Artificial Neural Network method was used to select the best subset of features affecting the mean weight diameter (MWD. In addition, the ability of ANNs and multiple linear regression (MLR) for quantifying the relationship between the MWD index and some soil properties was assessed. After the modeling process, the importance of the selected features in relation to spatial variability of aggregate stability was investigated. In order to prepare a suitable data set; MWD index and some soil features were measured in collected soils from 90 sampling points. Feature selection results showed that six soil features including clay, sand, organic matter, calcium carbonate, electrical conductivity, and sodium adsorption ratio had the greatest effect on the aggregates stability of the studied soils. According to the MWD modeling results, the obtained values of coefficient of determination (R2), mean absolute error percentage (MAEP), and root mean square error (RMSE) for the ANN model performance were 0.94, 21.39, and 0.07% respectively. These findings indicated that the developed ANN model was able to predict the complex and nonlinear relationships between the MWD index and the soil properties selected by the algorithm. Based on the sensitivity analysis results, calcium carbonate equivalent, sand particles, and organic matter were identified as key factors in estimating aggregate stability. Overall, this study provides a robust framework for the prediction of aggregate stability and identifying the most determinant parameters influencing it in arid and semi-arid soils that could be applied to other regions with similar challenges.