relation: https://khub.utp.edu.my/scholars/14948/
title: Development of models for oil and gas pipeline condition prediction using regression analysis
creator: Fadhli, M.
creator: Pedapati, S.R.
creator: Hamdan, H.
description: In order to maintain a pipeline in safe condition, frequent inspections are mandatory. However, inspection procedures that requires human operators are costly and time consuming due to high complexity of pipeline system. A good prediction models for oil and gas pipeline is essential for simulating and predicting the condition of a pipeline. Most prediction models lack the objectivity in predicting different failures types of pipelines due to solely focusing on single critical factor in their model. Regression model is used for modelling oil pipelines condition prediction. Two Linear models with different factors selection are developed in this study viz., Model 1 with factors comprises of mixture of continuous and discrete value, Model 2 with factors only restricted to continuous value. Results from this study shows that Model 2 yields better performance than Model 1 with performance of 95.56 compared to Model 1 with 83.03. © 2021 American Institute of Physics Inc.. All rights reserved.
publisher: American Institute of Physics Inc.
date: 2021
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Fadhli, M. and Pedapati, S.R. and Hamdan, H.  (2021) Development of models for oil and gas pipeline condition prediction using regression analysis.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105643032&doi=10.1063%2f5.0044773&partnerID=40&md5=5683670574d31bbbe8a1aa7190ec4408
relation: 10.1063/5.0044773
identifier: 10.1063/5.0044773