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Open AccessArticle

Comparing Single-Objective Optimization Protocols for Calibrating the Birds Nest Aquifer Model—A Problem Having Multiple Local Optima

1
Department of Civil and Environmental Engineering, Utah State University, Logan, UT 84322-4110, USA
2
College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 211100, Jiangsu, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(3), 853; https://doi.org/10.3390/ijerph17030853
Received: 1 January 2020 / Revised: 23 January 2020 / Accepted: 27 January 2020 / Published: 30 January 2020
(This article belongs to the Special Issue Water Resources Systems Quality and Quantity Management)
To best represent reality, simulation models of environmental and health-related systems might be very nonlinear. Model calibration ideally identifies globally optimal sets of parameters to use for subsequent prediction. For a nonlinear system having multiple local optima, calibration can be tedious. For such a system, we contrast calibration results from PEST, a commonly used automated parameter estimation program versus several meta-heuristic global optimizers available as external packages for the Python computer language—the Gray Wolf Optimization (GWO) algorithm; the DYCORS optimizer framework with a Radial Basis Function surrogate simulator (DRB); and particle swarm optimization (PSO). We ran each optimizer 15 times, with nearly 10,000 MODFLOW simulations per run for the global optimizers, to calibrate a steady-state, groundwater flow simulation model of the complex Birds Nest aquifer, a three-layer system having 8 horizontal hydraulic conductivity zones and 25 head observation locations. In calibrating the eight hydraulic conductivity values, GWO averaged the best root mean squared error (RMSE) between observed and simulated heads—20 percent better (lower) than the next lowest optimizer, DRB. The best PEST run matched the best GWO RMSE, but both the average PEST RMSE and the range of PEST RMSE results were an order of magnitude larger than any of the global optimizers. View Full-Text
Keywords: calibration; optimization; nonlinear optimization; groundwater; Grey Wolf Optimization; Particle Swarm Optimization; Meta-Heuristic Optimization; PEST calibration; Birds Nest Aquifer; Uinta Basin calibration; optimization; nonlinear optimization; groundwater; Grey Wolf Optimization; Particle Swarm Optimization; Meta-Heuristic Optimization; PEST calibration; Birds Nest Aquifer; Uinta Basin
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Lyons, R.T.; Peralta, R.C.; Majumder, P. Comparing Single-Objective Optimization Protocols for Calibrating the Birds Nest Aquifer Model—A Problem Having Multiple Local Optima. Int. J. Environ. Res. Public Health 2020, 17, 853.

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