Hyperparameter optimization approaches were applied to improve performance and accuracy of groundwater flow models. Freely available new software, NWTOPT, is described that uses Tree of Parzen Estimators (TPE) and Random Search algorithms to optimize MODFLOW-NWT's solver settings. We ran 3500 trials on a steady-state and transient model. To quantify the performance of candidate solver settings, we defined a loss function based on time elapsed and mass balance error of the MODFLOW-NWT forward run. Before optimization the steady-state model ran in ∼12 min and the transient model ran in ∼5 h with acceptable mass balance error (<1%). After optimization runtimes were reduced to ∼2.7 min (steady state) and ∼48 min (transient) with errors below 0.1%. In both cases TPE found hyperparameters that resulted in faster running and lower error models than those found by Random Search. The time to complete the optimization trials was also shorter with the TPE algorithm.

应用超参数优化方法来提高地下水流模型的性能和准确性。免费提供的新软件 NWTOPT 被描述为使用 Parzen 估计器树 (TPE) 和随机搜索算法来优化 MODFLOW-NWT 的求解器设置。我们在稳态和瞬态模型上进行了 3500 次试验。为了量化候选求解器设置的性能，我们根据 MODFLOW-NWT 正向运行的时间流逝和质量平衡误差定义了一个损失函数。在优化之前，稳态模型运行约 12 分钟，瞬态模型运行约 5 小时，质量平衡误差可接受（<1%）。优化后运行时间减少到~2.7 分钟（稳态）和~48 分钟（瞬态），误差低于 0.1%。在这两种情况下，TPE 发现的超参数比随机搜索发现的模型运行速度更快，错误模型更低。使用 TPE 算法完成优化试验的时间也更短。