Towards more accurate and explainable supervised learning-based prediction of deliverability for underground natural gas storage

Ali, AliyudaAliyuda, KachallaElmitwally, NouhMuhammad Bello, Abdulwahab2024-08-122024-08-122022-12-01Ali, A, Aliyuda, K, Elmitwally, N & Muhammad Bello, A 2022, 'Towards more accurate and explainable supervised learning-based prediction of deliverability for underground natural gas storage', Applied Energy, vol. 327, 120098. https://doi.org/10.1016/j.apenergy.2022.1200980306-2619https://hdl.handle.net/2164/24007Special thanks to the subject Editor and anonymous reviewers for their valuable time and insightful comments, which significantly improved the quality of this paper. Datasets related to this article can be found at https://www.eia.gov/naturalgas/ngqs/#?report = RP8&year1 = 2016&year2 = 2021&company = Name, an official repository of the U.S. Energy Information Administration (EIA).121810183engArtificial neural networkData-driven modelingInterpretable machine learningNatural gas industryRandom forestsSupport vector regressionTA Engineering (General). Civil engineering (General)Building and ConstructionMechanical EngineeringGeneral EnergyManagement, Monitoring, Policy and LawTATowards more accurate and explainable supervised learning-based prediction of deliverability for underground natural gas storageJournal article10.1016/j.apenergy.2022.120098https://www.scopus.com/pages/publications/85140272188