https://doi.org/10.4081/jae.2022.1283
Parameter estimation of soil water retention curve with Rao-1 algorithm
Published: 4 May 2022
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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The soil water retention curve (SWRC) has a significant role in determining the unsaturated properties of soil. A stochastic optimisation 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 determining soil water retention this model parameters in detail. In this study, the van Genuchten model is used to depict the SWRC for its good fitting capacity, and the van Genuchten model parameters are determined using Rao-1 algorithm. The feasibility and efficiency of the proposed 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 salp swarm algorithm, the particle swarm optimization algorithm, differential evolution algorithm, and RETC program. Through comparative analysis, Rao-1 algorithm outperforms other methods in determining SWRC parameters.
Borek Ł., Bogdał A. 2018. Soil water retention of the Odra river alluvial soils (Poland): estimating parameters by retc model and laboratory measurements. Appl. Ecol. Environ. Res. 16:4681-99.
Brooks R.H., Corey A.T.. 1964. Hydraulic properties of porous media and their relation to drainage design. Trans. ASAE 7:0026-8.
Campbell, G. S. 1974. A simple method for determining unsaturated conductivity from moisture retention data. Soil Sci. 117:311-4.
Duong V.-H., Bastawrous H.A., Lim K.C., See K.W., Peng Z., Dou S.X. 2015. Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares. J. Power Sour. 296:215-24.
Gardner W.R., Hillel D., Benyamini Y. 1970. Post-irrigation movement of soil water: 1. Redistribution. Water Resour. Res. 6:851-61.
Genuchten M.Th van. 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44:892-98.
Kassaye K.T., Boulange J., Lam Th.Van, Saito H., Watanabe H. 2020. Monitoring soil water content for decision supporting in agricultural water management based on critical threshold values adopted for andosol in the temperate monsoon climate. Agric. Water Manage. 229:105930.
Kawai K., Kato S., Karube D. 2000. The model of water retention curve considering effects of void ratio. In: Unsaturated soils for Asia. CRC Press, Boca Raton, FL, USA.
Li M.Y. 2018. Parameter estimation and nonlinear least-squares methods. In: Michael Y. Li (Ed.), An introduction to mathematical modeling of infectious diseases. Mathematics of Planet Earth. Cham: Springer International Publishing, Berlin, Germany, pp. 103-124.
Li Y.-B., Liu Y., Nie W.-B., Ma X.-Y. 2018. Inverse modeling of soil hydraulic parameters based on a hybrid of vector-evaluated genetic algorithm and particle swarm optimization. Water 10:84.
Maggi S. 2017. Estimating water retention characteristic parameters using differential evolution. Comput. Geotechn. 86:163-72.
Mirjalili S., Gandomi A.H., Mirjalili S.Z., Saremi S., Faris H., Mirjalili S.M. 2017. Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv. Engine. Softw. 114:163-91.
Nemes A., Schaap M.G., Leij F.J., Wösten J.H.M. 2001. Description of the unsaturated soil hydraulic database UNSODA Version 2.0. J. Hydrol. 251:151-62.
Rao R. 2016. Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Indust. Engine. Comput. 7:19-34.
Rao R.V. 2020. Rao algorithms: three metaphor-less simple algorithms for solving optimization problems. Int. J. Ind. Engine. Comput. 6:107-30.
Rossi C., Nimmo J.R. 1994. Modeling of soil water retention from saturation to oven dryness. Water Resour. Res. 30:701-8.
Silva M.L.d.N., Libardi P.L., Setti Gimenes F.H. 2018. Soil water retention curve as affected by sample height. Rev. Brasil. Ciênc. Do Solo 42:2018.
Storn R., Price K. 1997. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optimizat. 11:341-59.
Talat A.E., Galal M.E., Yeser A., Saad El-Dein A. A. 2020. Quantifying the hydraulic properties of some Egyptian soils using RETC Code. Arab Univ. J. Agric. Sci. [Epub ahead of print].
Tan F., Zhou W.-H., Yuen K.-V.. 2016. Modeling the soil water retention properties of same-textured soils with different initial void ratios. J. Hydrol. 542:731-43.
Venkata Rao R. 2019. Jaya optimization algorithm and its variants. In: Venkata Rao R. (Ed.), Jaya: an advanced optimization algorithm and its engineering applications. Cham: Springer International Publishing, Berlin, Germany, pp 9–58.
Wang D., Dapei T., Lei L. 2018. Particle swarm optimization algorithm: an overview. Soft Comput. 22:387-408.
Wang L., Chao H., Lingmiao H. 2018. Parameter estimation of the soil water retention curve model with Jaya algorithm. Comput. Electron. Agric. 151:349-53.
Wang L., Zijun Z., Chao H., Kwok Leung T. 2018. A GPU-accelerated parallel jaya algorithm for efficiently estimating Li-Ion battery model parameters. Appl. Soft Comput. 65:12-20.
Yang X., Xue Y.Y., Min J. 2012. Determining the soil water characteristic curve in term of van genuchten parameters by the particle swarm optimization. Appl. Mechan. Mater. 160:130-34.
Zhai Q., Harianto R., Alfrendo S., Guoliang D., Yan Z. 2020. Framework to estimate the soil-water characteristic curve for soils with different void ratios. Bull. Engine. Geol. Environment 79:4399-409.
Zhang J., Zhenhua W., Xiong L. 2018. Parameter estimation for soil water retention curve using the salp swarm algorithm. Water 10:815.
How to Cite
Wang, Z., Huang, C. and Wang, L. (2022) “Parameter estimation of soil water retention curve with Rao-1 algorithm”, Journal of Agricultural Engineering, 53(2). doi: 10.4081/jae.2022.1283.
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