Autocalibration experiments using machine learning and high performance computing
Using as example the Soil and Water Assessment Tool (SWAT) model and a Southern Ontario Canada watershed, we conduct a set of experiments on calibration using a manual approach, a parallelized version of the shuffled complex evolution (SCE), Generalized Likelihood Uncertainty Estimation (GLUE), Sequential Uncertainty Fitting (SUFI-2) and compare to a simple parallel search on a finite set of gridded input parameter values invoking the probably approximately correct (PAC) learning hypothesis. We derive an estimation of the error in fitting and a prior estimate of the probability of success, based on the PAC hypothesis. We conclude that from the equivalent effort expended on initial setup for the other named algorithms we can already find directly a good parameter set for calibration. We further note that, in this algorithm, simultaneous co-calibration of flow and chemistry (total nitrogen and total phosphorous) is more likely to produce acceptable results, as compared to flow first, even with a simple weighted multiobjective approach. This approach is especially suited to a parallel, distributed or cloud computational environment.
machine learning high performance computing swat automatic calibration multiobjective calibration probably approximately correct learning