A methodological framework for improving the performance of data-driven models, a case study for daily runoff prediction in the Maumee domain, U.S

Geoscientific models are simplified representations of complex earth and environmental systems (EESs). Compared with physics-based numerical models, data-driven modeling has gained popularity due mainly to data proliferation in EESs and the ability to perform prediction without requiring explicit mathematical representation of complex biophysical processes. However, because of the black-box nature of data-driven models, their performance cannot be guaranteed. To address this issue, we developed a generalizable framework for the improvement of the efficiency and effectiveness of model training and the reduction of model overfitting. This framework consists of two parts: hyperparameter selection based on Sobol global sensitivity analysis, and hyperparameter tuning using a Bayesian optimization approach. We demonstrated the framework efficacy through a case study of daily edge-of-field (EOF) runoff predictions by a tree-based data-driven model using eXtreme Gradient Boosting (XGBoost) algorithm in the Maumee domain, U.S. This framework contributes towards improving the performance of a variety of data-driven models and can thus help promote their applications in EESs.