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Water 2018, 10(1), 84; https://doi.org/10.3390/w10010084

Inverse Modeling of Soil Hydraulic Parameters Based on a Hybrid of Vector-Evaluated Genetic Algorithm and Particle Swarm Optimization

1,2
,
1,2
,
3
and
1,2,*
1
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
2
Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, China
3
State Key Laboratory Base of Eco-hydraulic Engineering in Arid Area, Xi’an University of Technology, Xi’an 710048, China
*
Author to whom correspondence should be addressed.
Received: 14 November 2017 / Revised: 25 December 2017 / Accepted: 15 January 2018 / Published: 18 January 2018
(This article belongs to the Special Issue Water and Solute Transport in Vadose Zone)
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Abstract

The accurate estimation of soil hydraulic parameters (θs, α, n, and Ks) of the van Genuchten–Mualem model has attracted considerable attention. In this study, we proposed a new two-step inversion method, which first estimated the hydraulic parameter θs using objective function by the final water content, and subsequently estimated the soil hydraulic parameters α, n, and Ks, using a vector-evaluated genetic algorithm and particle swarm optimization (VEGA-PSO) method based on objective functions by cumulative infiltration and infiltration rate. The parameters were inversely estimated for four types of soils (sand, loam, silt, and clay) under an in silico experiment simulating the tension disc infiltration at three initial water content levels. The results indicated that the method is excellent and robust. Because the objective function had multilocal minima in a tiny range near the true values, inverse estimation of the hydraulic parameters was difficult; however, the estimated soil water retention curves and hydraulic conductivity curves were nearly identical to the true curves. In addition, the proposed method was able to estimate the hydraulic parameters accurately despite substantial measurement errors in initial water content, final water content, and cumulative infiltration, proving that the method was feasible and practical for field application. View Full-Text
Keywords: inverse modeling; soil hydraulic properties; parameter estimation; multiobjective optimization; vector-evaluated genetic algorithm inverse modeling; soil hydraulic properties; parameter estimation; multiobjective optimization; vector-evaluated genetic algorithm
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Li, Y.-B.; Liu, Y.; Nie, W.-B.; Ma, X.-Y. Inverse Modeling of Soil Hydraulic Parameters Based on a Hybrid of Vector-Evaluated Genetic Algorithm and Particle Swarm Optimization. Water 2018, 10, 84.

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