TITLE: Machine learning modeling for reducing greenhouse gas emissions in urban areas
AUTHORS: Amel Kosovac, Adisa Medić, Muhamed Begović
ABSTRACT: The transport sector is one of the main contributors to global warming and carbon dioxide emissions and it is crucial to find sustainable solutions that balance economic, social, and environmental factors for long-term development. Machine learning and neural networks have the potential to significantly reduce greenhouse gas emissions in urban areas by optimizing transportation systems and reducing energy consumption. The main purpose of this paper is to identify the applications of machine learning and neural networks in reducing greenhouse gas emissions. The current topics of applying machine learning and neural networks in reducing greenhouse gas emissions have been synthesized. These technologies are useful for traffic prediction, prediction of the concentration of greenhouse gases, detection of environmental law violators, and route optimization, etc. Finally, recommendations for future research are at the end of the paper.
KEYWORDS: Machine Learning, Neural Networks, greenhouse gases, reduction, urban areas, city
PAGES: 131-136
DOI: 10.59478/ATCT.2023.18