21 Oct 2022
21 Oct 2022
Status: this preprint is open for discussion.

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

Yao Hu1,2,*, Chirantan Ghosh3,*, and Siamak Malakpour-Estalaki1 Yao Hu et al.
  • 1Department of Geography and Spatial Sciences, University of Delaware, Newark, DE, USA
  • 2Department of Civil and Environmental Engineering, University of Delaware, Newark, DE, USA
  • 3Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA
  • *These authors contributed equally to this work.

Abstract. 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.

Yao Hu et al.

Status: open (until 31 Dec 2022)

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Yao Hu et al.

Yao Hu et al.


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Short summary
Data-driven models (DDMs) gain popularity in earth and environmental systems due mainly to the advancements in data collection techniques and artificial intelligence (AI). The performance of such models is determined by the underlying machine learning (ML) algorithms. In this study, we develop a framework to improve the model performance by optimizing ML algorithms. We demonstrate the effectiveness of the framework using a DDM to predict edge-of-field runoff in the Maumee domain, U.S.