We agreed with the reviewer that the imbalanced data poses great challenges to model training.
#Daily domainer how to#
Can authors explain how to deal with the imbalanced data? I recommend the authors preprocess the data to improve the availability of the data in model training if the authors did not deal with the imbalanced data. Usually, this is very important for AI model training. (a) When discussing the hyperparameter SS, the paper mentioned that the daily EOF runoff is imbalanced data. Therefore, I think the manuscript can be published in EGUsphere after minor revision. In general, this manuscript is well-written, and the conclusion is reasonable.
And the case study for daily runoff prediction in the Maumee domain showed the good potential of this framework. Reviewer #1: This manuscript proposed an AI framework consisting of hyperparameter selection and training parts to improve training efficiency and reduce overfitting. Our responses to all the comments and suggestions are detailed below. We thank the reviewers for their insightful comments and constructive suggestions that have led to the improvement of our paper. This framework contributes towards improving the performance of a variety of data-driven models and can thus help promote their applications in EESs. 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 consists of two parts: hyperparameter selection based on Sobol global sensitivity analysis, and hyperparameter tuning using a Bayesian optimization approach. 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. However, because of the black-box nature of data-driven models, their performance cannot be guaranteed.
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. Geoscientific models are simplified representations of complex earth and environmental systems (EESs).
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