Determining the Soil Water Characteristic Curve in Term of Van Genuchten Parameters by the Particle Swarm Optimization

Yang, Xu; You, Xue Yi; Ji, Min

doi:10.4028/www.scientific.net/amm.160.130

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…(1) The optimization algorithms such as the nonlinear simplex method, Picard iterative approach, simplex evolutionary method and nonlinear damping least square were used to fit parameters of VG model [4,5].…”

Section: Introductionmentioning

confidence: 99%

Estimating Parameters of Van Genuchten Model for Soil Water Retention Curve by Intelligent Algorithms

Yang¹,

You²

2013

Appl. Math. Inf. Sci.

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An improved particle swarm optimization (IPSO) was proposed and the intelligent algorithms such as IPSO, genetic algorithm (GA), and simulated annealing algorithm (SA) were introduced to determine parameters of Van Genuchten (VG) model for soil water retention curve (SWRC) of four typical agricultural soil textures (clay, clay loam, silt loam and sand loam) in China. For comparison, the SWRC in term of VG model was also fitted by a computer program RETC and pedotransfer function Rosetta, respectively. For four soil textures, the value of determination coefficient (R 2 ) and root mean square error (RMSE) in the estimation of VG equation parameters by IPSO are the highest and lowest in the above three intelligent algorithms, respectively. The simulated values of water content by IPSO are much closed to the measured values (R 2 = 0.990). It was found that the Rosetta is unable to estimate the SWRC adequately and the highest RMSE value is up to 1.096E − 01cm 3 cm −3 . The predicted values of moisture content by Rosetta are far away from the measured values (R 2 = 0.585). The RETC provided good simulation results of water content (R 2 = 0.974). However, the soil residual water content (θ r ), of VG equation can not be obtained. It was concluded that the IPSO presented here is more reasonable and reliable to estimate the SWRC in term of VG model than the method of GA, SA, RETC and Rosetta.

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Section: Introductionmentioning

confidence: 99%

Estimating Parameters of Van Genuchten Model for Soil Water Retention Curve by Intelligent Algorithms

Yang¹,

You²

2013

Appl. Math. Inf. Sci.

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Parameter estimation of soil water retention curve with Rao-1 algorithm

2022

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The soil water retention curve (SWRC) has a significant role in determining the unsaturated properties of soil. A stochastic optimization algorithm named Rao-1 algorithm is employed to estimate the parameters of the SWRC model in this paper. The Rao-1 algorithm is a simple heuristic search algorithm containing only addition and multiplication operations. This paper introduces the method and its application in the determination of soil water retention model parameters in detail. In this study, van Genuchten model is used to depict the SWRC for its good fitting capacity, and parameters of the van Genuchten model are determined using Rao-1 algorithm. The feasibility and efficiency of the method are validated via the experimental results of 24 soil samples of 12 soil textural classes. Besides, the performance of Rao-1 algorithm is compared with that of Particle Swarm Optimization algorithm, Differential Evolution algorithm, and RETC program. Through comparative analysis, Rao-1 algorithm outperforms other methods in determining SWRC parameters.

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Determining the Soil Water Characteristic Curve in Term of Van Genuchten Parameters by the Particle Swarm Optimization

Cited by 2 publications

References 7 publications

Estimating Parameters of Van Genuchten Model for Soil Water Retention Curve by Intelligent Algorithms

Estimating Parameters of Van Genuchten Model for Soil Water Retention Curve by Intelligent Algorithms

Parameter estimation of soil water retention curve with Rao-1 algorithm

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