Improved validation framework and R-package for artificial neural network models
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 c...
| Main Authors: | , , , , , , |
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| Format: | Article |
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Elsevier
2017
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| Online Access: | https://eprints.nottingham.ac.uk/40959/ |
| _version_ | 1848796172288786432 |
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| author | Humphrey, Greer B. Maier, Holger R. Wu, Wenyan Mount, Nick J. Dandy, Graeme C. Abrahart, R.J. Dawson, C.W. |
| author_facet | Humphrey, Greer B. Maier, Holger R. Wu, Wenyan Mount, Nick J. Dandy, Graeme C. Abrahart, R.J. Dawson, C.W. |
| author_sort | Humphrey, Greer B. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:43:45Z |
| format | Article |
| id | nottingham-40959 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:43:45Z |
| publishDate | 2017 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-409592020-05-04T18:53:14Z https://eprints.nottingham.ac.uk/40959/ 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. 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. Elsevier 2017-06-30 Article PeerReviewed Humphrey, Greer B., Maier, Holger R., Wu, Wenyan, Mount, Nick J., Dandy, Graeme C., Abrahart, R.J. and Dawson, C.W. (2017) Improved validation framework and R-package for artificial neural network models. Environmental Modelling and Software, 92 . pp. 82-106. ISSN 1364-8152 Artificial neural networks; Multi-layer perceptron; R-package; Structural validation; Replicative validation; Predictive validation http://www.sciencedirect.com/science/article/pii/S136481521630963X doi:10.1016/j.envsoft.2017.01.023 doi:10.1016/j.envsoft.2017.01.023 |
| spellingShingle | Artificial neural networks; Multi-layer perceptron; R-package; Structural validation; Replicative validation; Predictive validation Humphrey, Greer B. Maier, Holger R. Wu, Wenyan Mount, Nick J. Dandy, Graeme C. Abrahart, R.J. Dawson, C.W. Improved validation framework and R-package for artificial neural network models |
| title | Improved validation framework and R-package for artificial neural network models |
| title_full | Improved validation framework and R-package for artificial neural network models |
| title_fullStr | Improved validation framework and R-package for artificial neural network models |
| title_full_unstemmed | Improved validation framework and R-package for artificial neural network models |
| title_short | Improved validation framework and R-package for artificial neural network models |
| title_sort | improved validation framework and r-package for artificial neural network models |
| topic | Artificial neural networks; Multi-layer perceptron; R-package; Structural validation; Replicative validation; Predictive validation |
| url | https://eprints.nottingham.ac.uk/40959/ https://eprints.nottingham.ac.uk/40959/ https://eprints.nottingham.ac.uk/40959/ |