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Parameter estimation of an improved van Genuchten soil water retention curve model using Gray Wolf Optimizer

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2026 ASABE Annual International Meeting  2600851.(doi:10.13031/aim.202600851)
Authors:   Yessica Alejandra Gómez-Pérez, Agustín Ruíz-García, Irineo L. López-Cruz, Miguel Servin-Palestina
Keywords:   Bio-inspired algorithms, improved gray wolf optimizer, model calibration, optimization techniques.

Abstract. The soil water retention curve (SWRC) is one of the most important hydraulic properties of soil and a crucial parameter in soil and water management for sustainable agricultural production. The van Genuchten model is the most widely used soil water retention curve model. The objective of this research was to estimate the parameters of the Improved van Genuchten (IvG) model for eight soil samples using the Improved Gray Wolf Optimizer (I-GWO) metaheuristic algorithm. The parameters were estimated by solving an optimization problem using the I-GWO algorithm, in which the error between predictions and experimental data was minimized by varying the model parameters. The accuracy of the results was assessed using the sum of squared errors (SSE), the root mean squared error (RMSE), and the Nash-Sutcliffe efficiency (EF) index. To assess the estimation performance, I-GWO algorithm was benchmarked with the lsqcurvefit function of MATLAB and Gray Wolf Optimizer (GWO) metaheuristic algorithms. The results showed that, based on the SSE, I-GWO outperformed lsqcurvefit in five samples and GWO in six samples. The average RMSE values were 0.0062, 0.0061 and 0.0082 cm³ cm⁻³ for lsqcurvefit, I-GWO and GWO, respectively. The minimum EF values were 0.992444, 0.992444 and 0.979895 for lsqcurvefit, I-GWO and GWO, respectively. These results demonstrate that the I-GWO algorithm was superior to the GWO and can be used to estimate the parameters of the improved van Genuchten model.

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