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Sensitivity analysis of radial basis function networks for river stage forecasting

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journal contribution
posted on 13.01.2021, 09:44 by Christian DawsonChristian Dawson
Sensitivity analysis of neural networks to input variation is an important research area as it goes some way to addressing the criticisms of their black-box behaviour. Such analysis of RBFNs for hydrological modelling has previously been limited to exploring perturbations to both inputs and connecting weights. In this paper, the backward chaining rule that has been used for sensitivity analysis of MLPs, is applied to RBFNs and it is shown how such analysis can provide insight into physical relationships. A trigonometric example is first presented to show the effectiveness and accuracy of this approach for first order derivatives alongside a comparison of the results with an equivalent MLP. The paper presents a real-world application in the modelling of river stage shows the importance of such approaches helping to justify and select such models.

History

School

  • Science

Department

  • Computer Science

Published in

Journal of Software Engineering and Applications

Volume

13

Issue

12

Pages

327 - 347

Publisher

Scientific Research Publishing, Inc.

Version

VoR (Version of Record)

Rights holder

© Author and Scientific Research Publishing Inc.

Publisher statement

This is an Open Access Article. It is published by Scientific Research Publishing under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

20/12/2020

Publication date

2020-12-23

Copyright date

2020

ISSN

1945-3116

eISSN

1945-3124

Language

en

Depositor

Dr Christian Dawson. Deposit date: 12 January 2021