Machine Learning and Deep Learning Analysis of Vehicle Carbon Footprint

Title

Machine Learning and Deep Learning Analysis of Vehicle Carbon Footprint

Creator

Dhyan R.; Joy H.K.; Sridevi R.; Electa Alice J.A.; Vanusha D.

Description

Clearly climate change is one of the most significant hazards to mankind nowadays. And daily the situation has become worse. No other way characterises climate change except through changes in the patterns of temperature and weather. Human activity generates the primary greenhouse gas emissions. Among these activities are burning coal, oil, natural gas, as well as other fuels; agricultural techniques, industrial operations, deforestation, burning coal, oil. Mostly resulting from human activities, the average temperature of the planet has significantly increased by almost 1.1 degrees Celsius since the late 1800s. One theory holds that internal combustion engines affect roughly thirteen percent. The objective of this work is to do an analysis of a complicated dataset involving fuel consumption in urban and highway environments as well as mixed combinations since the relevance of these variables in modelling attempts dictates. Reduced CO2 emissions emissions and environmental impact follow from reduced fuel use. The project used numerous machine learning and deep learning approaches to comprehend data analysis. Moreover, this work investigates the dataset to acquire knowledge and concurrently solves problems such overfitting and outliers. Control of complexity is achieved using several methods like VIF, PCA, and Cross-Validation. Models combining CNN and RNN performed really well with an accuracy of 0.99. The R-squared metrics are utilized in order to do the evaluation of the model. Apart from linear regression, support vector machines, Elastic Net with a rewardable accuracy, random forest was applied. It has rather good 0.98 accuracy. We can therefore state that our model analyzed the data properly and generated accurate output since the results we obtained during the assessment phase exactly the same ones we obtained during the training stage. Mass data cleansing is required as well as further study to increase machine learning model accuracy and performance. 2024 The authors.

Source

International Journal of Environmental Impacts, Vol-7, No. 2, pp. 287-292.

Date

2024-01-01

Publisher

International Information and Engineering Technology Association

Subject

climate change; fuel efficiency; greenhouse gas emissions; machine learning

Coverage

Dhyan R., Department of Computer Science, Christ University, Bangalore, 560029, India; Joy H.K., Department of Computer Science, Christ University, Bangalore, 560029, India; Sridevi R., Department of Computer Science, Christ University, Bangalore, 560029, India; Electa Alice J.A., Department of Electronics and Communication Engineering, KS Institute of Technology, Bangalore, 560019, India; Vanusha D., Department of Computer Science and Engineering, SRM Institute of Technology, Chennai, 603203, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 23982640

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.18280/ijei.070213

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201631162&doi=10.18280%2fijei.070213&partnerID=40&md5=205c7868572c386ae4be4b41d8dbd976