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dc.contributor.authorBeukes, Jacques Pieter
dc.contributor.authorDavel, Marelie Hattingh
dc.contributor.authorLotz, Stefan
dc.date.accessioned2021-03-18T09:29:34Z
dc.date.available2021-03-18T09:29:34Z
dc.date.issued2020
dc.identifier.issn1015-7999
dc.identifier.issn2313-7835
dc.identifier.urihttp://hdl.handle.net/10394/36917
dc.identifier.urihttps://doi.org/10.18489/sacj.v32i2.860
dc.description.abstractFeedforward neural networks provide the basis for complex regression models that produce accurate predictions in a variety of applications. However, they generally do not explicitly provide any information about the utility of each of the input parameters in terms of their contribution to model accuracy. With this is mind, we develop the pairwise network, an adaptation to the fully connected feedforward network that allows the ranking of input parameters according to their contribution to the model output. The application is demonstrated in the context of a space physics problem. Geomagnetic storms are multi-day events characterised by significant perturbations to the magnetic field of the Earth, driven by solar activity. Previous storm forecasting efforts typically use solar wind measurements as input parameters to a regression problem tasked with predicting a perturbation index such as the 1-minute cadence symmetric-H (Sym-H) index. We re-visit the task of predicting Sym-H from solar wind parameters, with two 'twists': (i) Geomagnetic storm phase information is incorporated as model inputs and shown to increase prediction performance. (ii) We describe the pairwise network structure and training process - first validating ranking ability on synthetic data, before using the network to analyse the Sym-H problem.en_US
dc.language.isoenen_US
dc.publisherSouth African Institute of Computer Scientists and Information Technologistsen_US
dc.subjectSpace Weatheren_US
dc.subjectInput Parameter Selectionen_US
dc.subjectNeural Networksen_US
dc.titlePairwise networks for feature ranking of a geomagnetic storm modelen_US
dc.typeArticleen_US


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