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

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

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)82-106
Number of pages25
JournalEnvironmental Modelling and Software
Publication statusPublished or Issued - 2017


  • Artificial neural networks
  • Multi-layer perceptron
  • Predictive validation
  • R-package
  • Replicative validation
  • Structural validation

ASJC Scopus subject areas

  • Software
  • Environmental Engineering
  • Ecological Modelling

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