A Flask-Based Web Application To Predict Co2 Emission In Vehicles Using Ml Techniques

DOI

10.2991/978-94-6463-250-7_12How to use a DOI?

Keywords

Carbon dioxide; Machine Learning; Flask; Random Forest Regression; Dataset

Abstract

Carbon Dioxide and other gases absorb sunlight and solar rays that have previously been mirroring off the earth’s surface as they accumulate in the atmosphere, causing global temperatures to rise. As a result, air pollution has advanced in several inhaling disorders and cardiac diseases among humans. Air pollution is also causing many effects on the international world by affecting soil fertility, air quality, and water quality. Car Pollution also forces the animals to abandon their habitat and move to a new place.

Passenger Vehicles also emit other gas pollutants, including nitrogen dioxide, carbon monoxide, and formaldehyde, that harm the global environment. Noise levels from vehicles due to the increasing city traffic also cause many hearing problems and psychological ill-health. One of the most challenging parts of the energy transition is lowering CO2 emissions from the transportation sector. Data is the critical element that enables algorithm training the most. With data, machine learning is more manageable for AI systems to perform. We use regression models for the prediction of the emission of CO2 from cars. The data, such as car details and features collected from the literature resources, are given as input to the ML model. The ML model predicts the amount of CO2 emitted from the car. The Road Transport Authority staff notifies the registered car owner to service the car if the estimated CO2 emission level is within the threshold value. This application program produces better outcomes by using many features of the car like fuel consumption, fuel transmission, engine size etc., as input and offers 24x7 service availability around the clock through internet connection to predict CO2 emission level.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - K. Mohana Prasad
AU  - K. Aravind
AU  - Arjun Singh Maru
PY  - 2023
DA  - 2023/10/17
TI  - A Flask-Based Web Application To Predict Co2 Emission In Vehicles Using Ml Techniques
BT  - Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)
PB  - Atlantis Press
SP  - 60
EP  - 64
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-250-7_12
DO  - 10.2991/978-94-6463-250-7_12
ID  - MohanaPrasad2023
ER  -