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|Title: ||Improved validation framework and R-package for artificial neural network models|
|Authors: ||Humphrey, Greer B.|
Maier, Holger R.
Mount, Nick J.
Dandy, Graeme C.
Abrahart, Robert J.
Dawson, Christian W.
|Keywords: ||Artificial neural networks|
|Issue Date: ||2017|
|Publisher: ||© Elsevier|
|Citation: ||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.|
|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.|
|Description: ||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.|
|Version: ||Accepted for publication|
|Publisher Link: ||https://doi.org/10.1016/j.envsoft.2017.01.023|
|Appears in Collections:||Published Articles (Computer Science)|
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