Application of Collective Intelligence of Artificial Bee Colony Algorithm in Optimization of Estimation and Zoning of Wind Erosion Intensity Using Geomorphological data (Case Study: Birjand Plain's Drainage Basin, South Khorasan Province)

The domain of wind erosion is wider than the other erosion processes, so the use of regional models is inevitable to estimate its intensity. The experimental models depend on components rated in defined ranges evaluating the amount of erosion. Different experiences and also variety of input components of the model lead to some inconsistency in the results, and decline the reliability of estimation. The aim of this study is to optimize the estimation of wind erosion in Birjand plain through removal and mitigation of the effects of different rating experiences. In this paper, the data obtained from the experimental model of Iranian Research Institute of Forest and Rangeland (IRIFR) are optimized using collective intelligence artificial bee colony algorithm. To achieve this purpose, after calculating the components of Iranian research institute of forest and rangeland model, the investigated area was divided into pixels of 200×200m. The pixels were located into 82 subdomains by using polar coordinates in order to decrease the computational time. Then optimization of bee colony algorithm was implemented in three steps: (1) the allocation process, (2) the investigation process and (3) conclusion process by the bees. Finally the pixels with greatest potential erosion were identified. About 49% of the area of wind erosion classes in IRIFR model moved to higher erosion classes in bee colony algorithm. Therefore bee colony algorithm is highly sensitive in the classification of wind erosion. The variance test of the erosion classes obtained by the two methods showed more reliability of bee colony results. The results showed the highest erosion rates occurred in the alluvial fan landforms and more than 90 percent of erosion centers are located in the pediment of geomorphologic unit.