Estimation of Bed Load Rate of a Gravel Bed River Using Evalutionary Systems and Classic Methods

Many mathematical classic methods have been suggested for predicting sediment transport in order to prevent or minimize damages caused by the erosion and sedimentation. These methods are mainly based on some statistical methods and field - laboratory data which have been suggested by many researchers such as Yang, White, Bagnold, and Hansen. The results obtained from the classical methods for predicting sediment transport revealed poor performance of these methods under different hydraulic conditions and the high sensitivity of them to river conditions and bed particle size. In this paper, performance of evolutionary systems for predicting the bed load transport in a gravel bed river was evaluated. For this purpose, the genetic programming method was used and the inputs parameters required for modeling were selected and optimized according to the parameters governing the classical methods as well as the basic concepts used in the structure of these methods. The data used herein was obtained from sampling Yazdekan Bridge station of Ghotourchay River which is located in West Azarbaijan and within the city limits of Khoy. In different stages of modeling, effect of different factors on performance of evolutionary systems was investigated and the optimized structure for each of these models was determined. Next, the results were compared with the classical methods. The obtained results showed high capability of intelligent methods comparing to other classic formulas for predicting the bed load in a gravel bed river