Dawson_Humphrey et al 2017 EMS ANN Validation Framework and R Package.pdf (2.98 MB)
Improved validation framework and R-package for artificial neural network models
journal contribution
posted on 2017-06-05, 13:18 authored by Greer B. Humphrey, Holger R. Maier, Wenyan Wu, Nick J. Mount, Graeme C. Dandy, Robert J. Abrahart, Christian DawsonChristian DawsonValidation 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.
History
School
- Science
Department
- Computer Science
Published in
Environmental Modelling and SoftwareVolume
92Pages
82 - 106Citation
HUMPHREY, G.B. ...et al., 2017. Improved validation framework and R-package for artificial neural network models. Environmental Modelling and Software, 92, pp. 82-106.Publisher
© ElsevierVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2017-01-30Publication date
2017-02-28Notes
This paper was published in the journal Environmental Modelling and Software and the definitive published version is available at https://doi.org/10.1016/j.envsoft.2017.01.023.ISSN
1364-8152eISSN
1873-6726Publisher version
Language
- en