Prediction of CO<SUB align="right">2 emission in transportation sector by computational intelligence techniques
Ömer Faruk Cansız, Kevser ÜNSALAN, Fatih Üneş
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.
Topics & Concepts
Adaptive neuro fuzzy inference systemSupport vector machineArtificial neural networkCarbon footprintFuzzy logicSustainabilityArtificial intelligenceComputer scienceComputational intelligenceMachine learningGreenhouse gasFuzzy control systemBiologyEcologyVehicle emissions and performance