Improved validation framework and R-package for artificial neural network models

Humphrey, Greer B.; Maier, Holger R.; Wu, Wenyan; Mount, Nick J.; Dandy, Graeme C.; Abrahart, R.J.; Dawson, C.W.

Abstract

Validation is a critical component of any modelling process. In artificial neural network (ANN) modelling, validation generally consists of the assessment of model predictive performance on an independent validation set (predictive validity). However, this ignores other aspects of model validation considered to be good practice in other areas of environmental modelling, such as residual analysis (replicative validity) and checking the plausibility of the model in relation to a priori system understanding (structural validity). In order to address this shortcoming, a validation framework for ANNs is introduced in this paper that covers all of the above aspects of validation. In addition, the validann R-package is introduced that enables these validation methods to be implemented in a user-friendly and consistent fashion. The benefits of the framework and R-package are demonstrated for two environmental modelling case studies, highlighting the importance of considering replicative and structural validity in addition to predictive validity.

Citation

Humphrey, G. B., Maier, H. R., Wu, W., Mount, N. J., Dandy, G. C., Abrahart, R., & Dawson, C. (2017). Improved validation framework and R-package for artificial neural network models. Environmental Modelling and Software, 92, https://doi.org/10.1016/j.envsoft.2017.01.023

• Journal Article Type: Article
• Acceptance Date: Jan 30, 2017
• Online Publication Date: Feb 28, 2017
• Publication Date: Jun 30, 2017
• Deposit Date: Mar 2, 2017
• Publicly Available Date: Mar 2, 2017
• Journal: Environmental Modelling and Software
• Print ISSN: 1364-8152
• Electronic ISSN: 1364-8152
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 92
• DOI: https://doi.org/10.1016/j.envsoft.2017.01.023
• Keywords: Artificial neural networks; Multi-layer perceptron; R-package; Structural validation; Replicative validation; Predictive validation
• Public URL: https://nottingham-repository.worktribe.com/output/870383
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S136481521630963X