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Please use this identifier to cite or link to this item: http://ir.library.ui.edu.ng/handle/123456789/5334
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dc.contributor.authorFalode, O.-
dc.contributor.authorUdomboso, C.-
dc.date.accessioned2021-05-25T10:33:05Z-
dc.date.available2021-05-25T10:33:05Z-
dc.date.issued2016-02-
dc.identifier.issn2161-7198-
dc.identifier.issn2161-718X-
dc.identifier.otherui_art_falode_predictive_2016-
dc.identifier.otherOpen Journal of Statistics 6, pp. 194-207-
dc.identifier.urihttp://ir.library.ui.edu.ng/handle/123456789/5334-
dc.description.abstractSince the discovery of oil and gas in Nigeria in 1956, much gas has been flared because the operators pay little or no concern to its utilization, and as such, trillions of dollars have been lost. In this paper, a model is proposed using Time Series Regression Model (TSRM) and Time Series Neural Network (TSNN) to model the production, utilization and flaring of natural gas in Nigeria with the ultimate aim of observing the trend of each activity. The results show that TSNN has better predictive and forecasting capabilities compared to TSRN. It is also observed that the higher the hidden neurons, the lower the error generated by the TSNN.en_US
dc.language.isoenen_US
dc.publisherScientifc Research Publishingen_US
dc.subjectNatural Gasen_US
dc.subjectProductionen_US
dc.subjectUtilizationen_US
dc.subjectFlaringen_US
dc.subjectTSRMen_US
dc.subjectTSNNen_US
dc.subjectModel Selectionen_US
dc.titlePredictive modeling of gas production, utilization and flaring in Nigeria using TSRM and TSNN: a comparative approachen_US
dc.typeArticleen_US
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