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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.


Greer B. Humphrey

Holger R. Maier

Wenyan Wu

Academic Director, university of Nottingham Online

Graeme C. Dandy

R.J. Abrahart

C.W. Dawson


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.


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,

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
Keywords Artificial neural networks; Multi-layer perceptron; R-package; Structural validation; Replicative validation; Predictive validation
Public URL
Publisher URL


ValidationFramework_HRM.pdf (3.2 Mb)

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