Application of a adaptive neuro-fuzzy technique for projection of the greenhouse gas emissions from road transportation

Alhindawi, R, Abu Nahleh, Y, Kumar, A and Shiwakoti, N 2019, 'Application of a adaptive neuro-fuzzy technique for projection of the greenhouse gas emissions from road transportation', Sustainability, vol. 11, no. 22, pp. 1-17.


Document type: Journal Article
Collection: Journal Articles

Title Application of a adaptive neuro-fuzzy technique for projection of the greenhouse gas emissions from road transportation
Author(s) Alhindawi, R
Abu Nahleh, Y
Kumar, A
Shiwakoti, N
Year 2019
Journal name Sustainability
Volume number 11
Issue number 22
Start page 1
End page 17
Total pages 17
Publisher MDPI AG
Abstract In the past, different forecasting models have been proposed to predict greenhouse gas (GHG) emissions. However, most of these models are unable to handle non-linear data. One of the most widely known techniques, the Adaptive Neuro-fuzzy inference system (ANFIS), can deal with nonlinear data. Its ability to predict GHG emissions from road transportation is still unexplored. This study aims to fulfil that gap by adapting the ANFIS model to predict GHG emissions from road transportation by using the ratio between vehicle-kilometers and number of transportation vehicles for six transportation modes (passenger cars, motorcycle, light trucks, single-unit trucks, tractors, and buses) from the North American Transportation Statistics (NATS) online database over a period of 22 years. The results show that ANFIS is a suitable method to forecast GHG emissions from the road transportation sector.
Subject Transport Engineering
Keyword(s) Adaptive neuro-fuzzy inference system (ANFIS)
Fuzzy inference system (FIS)
Greenhouse gas emissions
Modeling
Neuro-fuzzy
Transport systems
DOI - identifier 10.3390/su11226346
Copyright notice © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
ISSN 2071-1050
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