@inproceedings{scholars14948,
           title = {Development of models for oil and gas pipeline condition prediction using regression analysis},
       publisher = {American Institute of Physics Inc.},
         journal = {AIP Conference Proceedings},
            note = {cited By 1; Conference of 6th International Conference on Green Design and Manufacture 2020, IConGDM 2020 ; Conference Date: 23 July 2020 Through 24 July 2020; Conference Code:168752},
          volume = {2339},
             doi = {10.1063/5.0044773},
            year = {2021},
        abstract = {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. {\^A}{\copyright} 2021 American Institute of Physics Inc.. All rights reserved.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105643032&doi=10.1063\%2f5.0044773&partnerID=40&md5=5683670574d31bbbe8a1aa7190ec4408},
            isbn = {9780735440913},
          author = {Fadhli, M. and Pedapati, S. R. and Hamdan, H.},
            issn = {0094243X}
}