A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networks

Abstract

Data splitting is an important step in the artificial neural network (ANN) development process, whereby the available data are divided into training, testing, and validation subsets to ensure good generalization ability of the model. Considering that only one split of the data is typically used when developing ANN models, data splitting has a significant impact on model performance, depending on which data are allocated to the three subsets. Therefore, it is important to find a data splitting method that consistently results in predictive validation errors that are representative of the predictive errors obtained over the full range of the available data. This paper addresses this issue by i..