The contribution of AI-based approaches in the determination of CO2 emission gas amounts of vehicles, determination of CO2 emission rates yearly of countries, air quality measurement and determination of smart electric grids' stability

Abstract

To make the globalized world more livable, many countries have started to take various measures and have made the necessary legal arrangements in this regard. Any initiative that can harm the ecological balance of countries on a global scale harms both living things and the country's economy. Today, technological infrastructure services play a more environmentally friendly role. In a world where people live more intensely, it is now possible to provide automatic control of systems that can harm the environment with AI-based technologies. Four datasets are used in this paper. The first dataset includes vehicle features in the calculation of the CO2 emission gas amount of vehicles. The second dataset shows the annual CO2 emission gas measurement amounts of the countries. The third dataset consists of air quality measurement data and the fourth dataset contains data created on the determination of the stability of smart electricity networks. Proposed approaches consist of machine learning methods and AI models designed using open-source codes. The models designed for this study are as follows: long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-based LSTM model, and recurrent neural network (RNN). It has been observed that proposed approaches contribute to all the experimental analyzes. © 2022 Scrivener Publishing LLC. All rights reserved.