Groundwater parameter estimation using multiquadric-based meshfree simulation with covariance matrix adaptation evolution strategy optimization for a regional aquifer system

Patel, Sharad; Eldho, T. I.; Rastogi, A. K.; Rabinovich, Avinoam

doi:10.1007/s10040-022-02544-y

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…Finally, CMAES is another global optimization algorithm including stochastic approaches and non-linear functions. It uses the maximum likelihood method to attain parameter values, thereby giving results closer to the optimum solution in previous iterations [33].…”

Section: Calibration Algorithmsmentioning

confidence: 99%

Effect of Model Structure and Calibration Algorithm on Discharge Simulation in the Acısu Basin, Turkey

Alp

Demirel

Aşıkoğlu

2022

Climate

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In this study, the Acısu Basin—viz., the headwater of the Gediz Basin—in Turkey, was modelled using three types of hydrological models and three different calibration algorithms. A well-known lumped model (GR4J), a commonly used semi-distributed (SWAT+) model, and a skillful distributed (mHM) hydrological model were built and integrated with the Parameter Estimation Tool (PEST). PEST is a model-independent calibration tool including three algorithms—namely, Levenberg Marquardt (L-M), Shuffled Complex Evolution (SCE), and Covariance Matrix Adoption Evolution Strategy (CMA-ES). The calibration period was 1991–2000, and the validation results were obtained for 2002–2005. The effect of the model structure and calibration algorithm selection on the discharge simulation was evaluated via comparison of nine different model-algorithm combinations. Results have shown that mHM and CMA-ES combination performed the best discharge simulation according to NSE values (calibration: 0.67, validation: 0.60). Although statistically the model results were classified as acceptable, the models mostly missed the peak values in the hydrograph. This problem may be related to the interventions made in 2000–2001 and may be overcome by changing the calibration and validation periods, increasing the number of iterations, or using the naturalized gauge data.

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Section: Calibration Algorithmsmentioning

confidence: 99%