Publication:
Earthquake intensity estimation via an artificial neural network: Examination of different network designs and training algorithms

dc.contributor.authorSağlam, Aslı Sebatlı
dc.contributor.authorÇavdur, Fatih
dc.contributor.buuauthorSağlam, Aslı Sebatlı
dc.contributor.buuauthorÇAVDUR, FATİH
dc.contributor.departmentBursa Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.
dc.contributor.orcid0000-0002-9445-6740
dc.contributor.researcheridAAC-2099-2020
dc.contributor.researcheridJYP-7925-2024
dc.date.accessioned2024-09-25T11:41:57Z
dc.date.available2024-09-25T11:41:57Z
dc.date.issued2022-01-01
dc.description.abstractPurpose: We aim to estimate the earthquake intensity via an artificial neural network. Theory and Methods: We obtain significant earthquakes data from the database of the United States Geological Survey. An artificial neural network is developed using the MATLAB Neural Network Toolbox. We first determine an appropriate network design by estimating earthquake intensity with different artificial neural network designs and then the best training algorithm for the appropriate network design by evaluating different algorithms for the corresponding network design. Results: In terms of the average performance parameters, the network structure with two hidden layers and five and ten hidden neurons in each respective layer is determined as the most appropriate design. We observe the best results in terms of performance parameters by using the Levenberg-Marquardt training algorithm with Bayesian Regularization for the corresponding network structure. Conclusion: Earthquake intensity estimation is critical in predicting the impact that will occur after a disaster. In this study, we estimate earthquake intensity via an artificial neural network. In future studies, associated with earthquake intensity, we can estimate the number of casualties, damages to the buildings, economic loss and so on. Integrating earthquake intensity estimation into other disaster operation management studies may be another future study direction.
dc.identifier.doi10.17341/gazimmfd.791337
dc.identifier.endpage2145
dc.identifier.issn1300-1884
dc.identifier.issue4
dc.identifier.startpage2133
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.791337
dc.identifier.urihttps://dergipark.org.tr/tr/pub/gazimmfd/issue/68677/791337
dc.identifier.urihttps://hdl.handle.net/11452/45237
dc.identifier.volume37
dc.identifier.wos000767316300029
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherGazi Üniversitesi
dc.relation.journalJournal of The Faculty of Engineering and Architecture of Gazi University
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.relation.tubitak115M020
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMagnitude prediction
dc.subjectDamage
dc.subjectCasualties
dc.subjectLogistics
dc.subjectModel
dc.subjectDisaster operations management
dc.subjectDisaster relief operations
dc.subjectMachine learning
dc.subjectArtificial neural networks
dc.subjectEarthquake intensity estimation
dc.subjectEngineering
dc.titleEarthquake intensity estimation via an artificial neural network: Examination of different network designs and training algorithms
dc.typeArticle
dspace.entity.typePublication
relation.isAuthorOfPublication488d40a8-9d9d-4814-89f3-0a6433d547cc
relation.isAuthorOfPublication.latestForDiscovery488d40a8-9d9d-4814-89f3-0a6433d547cc

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