Title: Prediction of CO2 emission in transportation sector by computational intelligence techniques
Authors: Omer Faruk Cansiz; Kevser Unsalan; Fatih Unes
Addresses: Department of Civil Engineering, Iskenderun Technical University, Turkey ' Department of Civil Engineering, Iskenderun Technical University, Turkey ' Department of Civil Engineering, Iskenderun Technical University, Turkey
Abstract: Carbon footprint is considered the main cause of global warming. There are various studies on environmental sustainability carried out global scale. In this study, prediction models were developed for CO2 emissions in transportation sector. Artificial neural networks (ANN), simple membership functions and fuzzy rule generation technique (SMRGT), support vector machine (SVM) and adaptive neuro fuzzy inference system (ANFIS) methods, which are artificial intelligence techniques (AI), and also multiple linear regression (MLR), which is a statistical method, were used for the analysis. As a result of the comparison the best performance was seen in ANN model.
Keywords: carbon footprint; green logistic; transport emissions; CO2 emissions.
International Journal of Global Warming, 2022 Vol.27 No.3, pp.271 - 283
Received: 09 Nov 2020
Accepted: 09 Dec 2021
Published online: 18 Jul 2022 *