Early termination strategies with asynchronous parallel optimization in application to automatic calibration of groundwater PDE models

Automatic calibration is widely used to estimate parameters in hydrological models. The main idea is to use optimization algorithms to minimize the discrepancy between field data and simulation prediction. This process involves iterative exchanges of a parameter set chosen by the optimization and simulation. However, for computationally expensive models, such as groundwater flow and transport models, the calibration process may be extremely computationally demanding. In this study, we introduce and demonstrate a new asynchronous parallel surrogate-assisted optimization algorithm with an early truncation feature and different knowledge extraction strategies (SO-AET-k). Results show that our asynchronous algorithm performs significantly better than the synchronous analogue (SO-SP), requiring only 40%-70% of the computation time to achieve the same averaged results. In addition, various knowledge extraction strategies of the asynchronous SO-AET-k are tested. In comparisons with two other algorithms, SCE-UA and APPSPACK, asynchronous SO-AET-k shows significantly better performance in terms of both efficiency and robustness.

Automated summary

Automatic calibration is widely used in hydrological models to estimate parameters by minimizing the discrepancy between field data and simulation. This study introduces a new asynchronous parallel surrogate-assisted optimization algorithm, showing significantly better performance in efficiency and robustness compared to other algorithms. This asynchronous algorithm achieves the same results with 40%-70% less computation time than its synchronous counterpart.