Proceedings Paper 2024 · 1 atıf

The Genetic Algorithm-Artificial Neural Networks Integration in the Optimization: An Application for Transportation Systems

Caparoglu, Omer Faruk, Ok, Yesim, Ozaydin, Nadide Caglayan

Özet

The transportation sector stands as one of the broadest and most significant industries worldwide. Today, one of the key environmental consequences of the transportation sector is the increase in carbon dioxide (CO2) emissions. This research focuses on CO2 emissions specifically from road transportation. The primary objectives of the study are to investigate how yearly fluctuations on highways impact CO2 emissions. The study utilizes genetic algorithm-based artificial neural networks (GA-ANN) to design a transportation optimization model. Artificial neural networks are employed to estimate the proportion of CO2 emissions from roadways in the total transportation-related CO2 emissions. The article proposes an optimization method using genetic algorithms (GA) to optimize the parameters of the artificial neural networks. The development of the ANN model was carried out in the MATLAB program. The constructed GA-ANN model was compared to a variety of other machine learning (ML) models linear regression (LR), support vector machine (SVM), random forest (RF), artificial neural network (ANN).

Anahtar Kelimeler

Artificial Neural Network Genetic Algorithm Transportation Nature-Inspired Optimization Algorithms Optimization Machine Learning Greenhouse Gas Emission EMISSIONS

WoS Kategorileri

Computer Science, Artificial Intelligence Computer Science, Interdisciplinary Applications Computer Science, Theory & Methods

Sürdürülebilir Kalkınma Amaçları

Sürdürülebilir Şehirler ve Topluluklar

Künye

Dergi: INTELLIGENT AND FUZZY SYSTEMS, INFUS 2024 CONFERENCE, VOL 1
Yıl: 2024
Cilt: 1088
Sayfa: 277-284
ISSN: 2367-3370
eISSN: 2367-3389
DOI: 10.1007/978-3-031-70018-7_30
WoS UID: WOS:001331332200030

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