dc.contributor.author | Cansız, Ömer Faruk | |
dc.contributor.author | Üneş, Fatih | |
dc.contributor.author | Erginer, İbrahim | |
dc.contributor.author | Taşar, Bestami | |
dc.date.accessioned | 2022-01-04T06:44:06Z | |
dc.date.available | 2022-01-04T06:44:06Z | |
dc.date.issued | 2021 | en_US |
dc.identifier.citation | Cansiz, Ö.F., Üneş, F., Erginer, I., Taşar, B. (2021). Modeling of highways energy consumption with artificial intelligence and regression methods. International Journal of Environmental Science and Technology.
https://doi.org/10.1007/s13762-021-03813-1 | en_US |
dc.identifier.uri | https://doi.org/10.1007/s13762-021-03813-1 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12508/2045 | |
dc.description.abstract | While developing technology and industrialization factors increase production, they also lead to an increase in energy consumption at the same time. The transportation sector, which is a branch of industrialization, has an important place on the basis of sector in energy consumption. In this study, energy consumption in the transportation sector has been examined, especially in the USA, where freight transport by road has an important place, it has a high potential. Within the scope of the study, energy consumption prediction modeling is made by using artificial neural networks (ANN) adaptive neuro-fuzzy inference system (ANFIS) and Simple Membership Functions and Fuzzy Rules Generation Technique (Fuzzy SMRGT) from artificial intelligence techniques. Artificial intelligence methods were also compared with multivariate linear regressions and multivariate regressions types. Interaction, pure quadratic and quadratic methods were used as multiple nonlinear regression. In the modeling, energy consumption was estimated by taking the highway network length, the number of vehicles and the number of drivers as independent variables. When comparing the prediction models, the determination coefficient (R-2), the root-mean-square error (RMSE) and the average percentage error (APE) performance criteria were taken into consideration. In addition, it was shown that the models performed well based on the metrics in the testing phase. When the performances of the models were compared, it was seen that two models obtained remarkable results. According to performance criteria, the best model is obtained by Fuzzy SMRGT and ANFIS methods. R-2, RMSE, APE values of the best models are Fuzzy SMRGT (0,978; 208,08; % 0,79) and ANFIS (0,969; 282,69; % 1,06), respectively. The Fuzzy SMGRT and ANFIS models have slightly better performance than MLR, MR, ANN models. It is aimed to use the developed models in the evaluation and management of transportation and energy policies. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.relation.isversionof | 10.1007/s13762-021-03813-1 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | ANFIS | en_US |
dc.subject | ANN | en_US |
dc.subject | Energy consumption | en_US |
dc.subject | Fuzzy SMRGT | en_US |
dc.subject | Prediction | en_US |
dc.subject | Road transportation | en_US |
dc.subject.classification | Environmental Sciences & Ecology | |
dc.subject.classification | Stream Flow | |
dc.subject.classification | Flood Forecasting | |
dc.subject.classification | Water Tables | |
dc.subject.other | Forecasting | |
dc.subject.other | Freight transportation | |
dc.subject.other | Fuzzy inference | |
dc.subject.other | Fuzzy neural networks | |
dc.subject.other | Fuzzy systems | |
dc.subject.other | Mean square error | |
dc.subject.other | Membership functions | |
dc.subject.other | Motor transportation | |
dc.subject.other | Regression analysis | |
dc.subject.other | Roads and streets | |
dc.subject.other | Well testing | |
dc.subject.other | Adaptive neuro-fuzzy inference | |
dc.subject.other | Adaptive neuro-fuzzy inference system | |
dc.subject.other | Artificial intelligence methods | |
dc.subject.other | Energy-consumption | |
dc.subject.other | Fuzzy SMRGT | |
dc.subject.other | Industrialisation | |
dc.subject.other | Neuro-fuzzy inference systems | |
dc.subject.other | Prediction modelling | |
dc.subject.other | Road transportation | |
dc.subject.other | Transportation sector | |
dc.subject.other | Dam-Reservoir level | |
dc.subject.other | Dam-Reservoir level | |
dc.subject.other | Carbon emissions | |
dc.subject.other | Economic-Growth | |
dc.subject.other | Co2 emissions | |
dc.title | Modeling of highways energy consumption with artificial intelligence and regression methods | en_US |
dc.type | article | en_US |
dc.relation.journal | International Journal of Environmental Science and Technology | en_US |
dc.contributor.department | Mühendislik ve Doğa Bilimleri Fakültesi -- İnşaat Mühendisliği Bölümü | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.contributor.isteauthor | Cansız, Ömer Faruk | |
dc.contributor.isteauthor | Üneş, Fatih | |
dc.contributor.isteauthor | Erginer, İbrahim | |
dc.contributor.isteauthor | Taşar, Bestami | |
dc.relation.index | Web of Science - Scopus | en_US |
dc.relation.index | Web of Science Core Collection - Science Citation Index Expanded | |