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Broad learning for nonparametric spatial modeling with application to seismic attenuation
Sin-Chi Kuok1,2; Ka-Veng Yuen1
Source PublicationComputer-Aided Civil and Infrastructure Engineering

Spatial modeling is a core element in geographical information science. It incorporates geographic information to construct the relationship for interpreting the behavior of spatial phenomena. In this paper, a broad learning framework for nonparametric spatial modeling is presented. Broad learning overcomes the obstacle of expensive computational consumption in deep learning and provides a powerful computationally efficient alternative. In contrast to the deep learning architecture that is configured with stacks of hierarchical layers, broad learning networks are established in a flat manner that can be flexibly reconfigured with the inherited information from the trained network. To develop the broad learning network, a simple prototype network is established as the initial trial and it is modified incrementally to enhance its data fitting capacity. Consequently, complex relationship of unstructured spatial data can be modeled efficiently. To demonstrate the efficacy and applicability of the broad learning framework, we will present a simulated example and a real application using the strong ground motion records on the 2008 great Wenchuan earthquake.

URLView the original
Indexed BySCIE
WOS Research AreaComputer Science ; Construction & Building Technology ; Engineering ; Transportation
WOS SubjectComputer Science, Interdisciplinary Applications ; Construction & Building Technology ; Engineering, Civil ; Transportation Science & Technology
WOS IDWOS:000482593300001
PublisherWILEY, 111 RIVER ST, HOBOKEN 07030-5774, NJ
Scopus ID2-s2.0-85071133182
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Cited Times [WOS]:17   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorKa-Veng Yuen
Affiliation1.State Key Laboratory on Internet of Things for Smart City and Department of Civil and Environmental Engineering,University of Macau,China
2.Department of Engineering Science,University of Oxford,Oxford,United Kingdom
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Sin-Chi Kuok,Ka-Veng Yuen. Broad learning for nonparametric spatial modeling with application to seismic attenuation[J]. Computer-Aided Civil and Infrastructure Engineering,2019,35(3):203-218.
APA Sin-Chi Kuok,&Ka-Veng Yuen.(2019).Broad learning for nonparametric spatial modeling with application to seismic attenuation.Computer-Aided Civil and Infrastructure Engineering,35(3),203-218.
MLA Sin-Chi Kuok,et al."Broad learning for nonparametric spatial modeling with application to seismic attenuation".Computer-Aided Civil and Infrastructure Engineering 35.3(2019):203-218.
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