Journal article

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

ENVIRONMENTAL MODELLING & SOFTWARE | ELSEVIER SCI LTD | Published : 2017

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 ..

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University of Melbourne Researchers