Department of Civil and Environmental Engineering, Utah State University, Logan, UT 84322-4110, USA.
College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 211100, Jiangsu, China.
Int J Environ Res Public Health. 2020 Jan 30;17(3):853. doi: 10.3390/ijerph17030853.
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.
为了更好地反映实际情况，环境和健康相关系统的模拟模型可能是非线性的。模型校准理想情况下确定了用于后续预测的全局最优参数集。对于具有多个局部最优值的非线性系统，校准可能会很繁琐。对于这样的系统，我们对比了 PEST 的校准结果，PEST 是一个常用的自动化参数估计程序，以及几个作为 Python 计算机语言外部包提供的元启发式全局优化器——灰狼优化（GWO）算法；具有径向基函数代理模拟器（DRB）的 DYCORS 优化器框架；以及粒子群优化（PSO）。我们运行了每个优化器 15 次，对于全局优化器，每次运行大约有 10,000 次 MODFLOW 模拟，以校准复杂的鸟巢含水层的稳态地下水流动模拟模型，该模型是一个具有 8 个水平水力传导率带和 25 个水头观测点的三层系统。在对 8 个水力传导率值进行校准时，GWO 平均了观测水头和模拟水头之间的最佳均方根误差（RMSE）-比下一个最低的优化器 DRB 低 20%。最佳的 PEST 运行与最佳的 GWO RMSE 相匹配，但 PEST 的平均 RMSE 和 PEST RMSE 结果的范围都比任何全局优化器大一个数量级。
