Differential Evolution for Prediction of Longitudinal Dispersion Coefficients in Natural Streams

Differential evolution (DE) is a population-based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces, and has been successfully used to solve several kinds of problems. In this paper, a novel expression for the prediction of longitudinal dispersion coefficient in natural streams is proposed to minimize the sum-square error using differential evolution algorithm. The new expression considers the hydraulic and geometric characteristics of rivers. Datasets consisting 65 sets of observations from 29 rivers in the unite states are used to test the proposed algorithm, and results demonstrate the performance and applicability of the proposed differential evolution. Compared with the previous methods, the new expression using differential evolution is superior to other expressions. Moreover, 56.92 % of the prediction using the new expression lie with the 0.5 < K _pre /K _meas  < 1.5 that is better than other expressions.