Predicting Longitudinal Dispersion Coefficients in Sinuous Rivers by Genetic Algorithm

Abstract

In the present work, existing empirical expressions for longitudinal dispersion coefficient of rivers (K) are evaluated. They are found inadequate primarily because these expressions ignore the channel sinuosity, an important parameter representing a river’s transverse irregularities that affect mixing process. Hence, a new expression for K is derived taking into account the sinuosity besides few of other hydraulic and geometric characteristics of a river. The model makes use of genetic algorithm (GA) on published field data. Based on several performance indices, the new expression is found superior to many existing expressions for estimating K. The sensitivity and error analysis conducted on parameters of the new expression show the channel sinuosity an important input for predicting K accurately. Any error in measurement of sinuosity would lead to significant deviation in the longitudinal dispersion coefficient in sinuous rivers.