Predicting the occurrence of adverse events using an adaptive neuro-fuzzy inference system (ANFIS) approach with the help of ANFIS input selection

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dc.contributor.authorÇakıt, Erman
dc.contributor.authorKarwowski, Waldemar
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-29T19:29:18Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-29T19:29:18Z
dc.date.issued2017
dc.departmentMühendislik Fakültesi
dc.description.abstractThis study presents an adaptive neuro-fuzzy inference system (ANFIS) approach performed to estimate the number of adverse events where the dependent variables are adverse events leading to four types of variables: number of people killed, wounded, hijacked and total number of adverse events. Fourteen infrastructure development projects were selected based on allocated budgets values at different time periods, population density, and previous month adverse event numbers selected as independent variables. Firstly, number of independent variables was reduced by using ANFIS input selection approach. Then, several ANFIS models were performed and investigated for Afghanistan and the whole country divided into seven regions for analysis purposes. Performances of models were assessed and compared based on the mean absolute errors. The difference between observed and estimated value was also calculated within range with values around 90 %. We included multiple linear regression (MLR) model results to assess the predictive power of the ANFIS approach, in comparison to a traditional statistical approach. When the model accuracy was calculated according to the performance metrics, ANFIS showed greater predictive accuracy than MLR analysis, as indicated by experimental results. As a result of this study, we conclude that ANFIS is able to estimate the occurrence of adverse events according to economical infrastructure development project data.
dc.description.sponsorshipOffice of Naval Research (ONR) [1052339]
dc.description.sponsorshipThe authors are grateful for the support of the Office of Naval Research (ONR) under Grant No. 1052339 and the helpful guidance of ONR Program Management. The authors also gratefully acknowledge the Editor and anonymous reviewers for their constructive and helpful comments.
dc.identifier.doi10.1007/s10462-016-9497-3
dc.identifier.endpage155en_US
dc.identifier.issn0269-2821
dc.identifier.issn1573-7462
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage139en_US
dc.identifier.urihttps://doi.org/10.1007/s10462-016-9497-3
dc.identifier.urihttps://hdl.handle.net/20.500.12451/6152
dc.identifier.volume48en_US
dc.identifier.wosWOS:000404634500001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofArtificial Intelligence Review
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectEconomical Infrastructure Development
dc.subjectAdverse Events
dc.subjectAdaptive Neuro-fuzzyinference Systems (ANFIS)
dc.subjectMultiple Linear Regression ( MLR)
dc.titlePredicting the occurrence of adverse events using an adaptive neuro-fuzzy inference system (ANFIS) approach with the help of ANFIS input selection
dc.typeArticle

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