Utilization of Particle Swarm Optimization Method to Determine Kinematic–Dispersive Wave Model Coefficients for Prediction of the Preferential Water Flow in Soil

Authors

1 Ph.D. Graduate, Dept. of Water Engineering, Faculty of Water Sciences Engineering, Shahid Chamran Univ. of Ahvaz, Khuzestan, Iran

2 Prof., Faculty of Water Sciences Engineering, Shahid Chamran Univ. of Ahvaz, Khuzestan, Iran

3 Assoc. Prof., Dept. of Water Engineering, Faculty of Agricultural Sciences, Univ. of Guilan, Rasht, Guilan, and Dept. of Water Engineering and Environment, Caspian Sea Basin Research Center, Iran.

Abstract

Due to the rapid movement of water and contaminants through preferential flow paths, in this study kinematic– dispersive wave model as an appropriate method to simulate this motion was used. This model had three unknown coefficients which were determined using particle swarm optimization (PSO) method. Four different rainfall intensities of 56.97, 107.64, 133.01, and 161.71 mm h-1 were applied on the surface of a soil column and output water fluxes from the bottom of the soil column versus the soil mobile moisture amount in the column were recorded. Model coefficients were calculated by minimizing the error function between the observed values and the equation of the flow flux prediction. To achieve the best results and the minimum amount of error function, several solutions were evaluated and different values for c1 and c2 that control the best personal and global, respectively and interfere to make the next generation of results were tested. The best values for c1 and c2 were 1.2 and 2.4, respectively. Also to find the best results, several equations as the inertia weight, to control the particles velositeis in the search spaces, were tested and finally the linear decreasing inertia weight was chosen. Generally the results showed that the used algorithm could define the coefficients of kinematic–dispersive wave model in a short time and with a reasonable accuracy.
 

Keywords

References

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Water and Soil Science
Volume 28, Issue 3
November 2018
Pages 1-12

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