Dataset Information

Forecasting CO₂ emissions of fuel vehicles for an ecological world using ensemble learning, machine learning, and deep learning models.

ABSTRACT:

Background

The continuous increase in carbon dioxide (CO₂) emissions from fuel vehicles generates a greenhouse effect in the atmosphere, which has a negative impact on global warming and climate change and raises serious concerns about environmental sustainability. Therefore, research on estimating and reducing vehicle CO₂ emissions is crucial in promoting environmental sustainability and reducing greenhouse gas emissions in the atmosphere.

Methods

This study performed a comparative regression analysis using 18 different regression algorithms based on machine learning, ensemble learning, and deep learning paradigms to evaluate and predict CO₂ emissions from fuel vehicles. The performance of each algorithm was evaluated using metrics including

SUBMITTER: Gurcan F

PROVIDER: S-EPMC11323052 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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Dataset Information

Forecasting CO₂ emissions of fuel vehicles for an ecological world using ensemble learning, machine learning, and deep learning models.

Background

Methods

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Forecasting CO2 emissions of fuel vehicles for an ecological world using ensemble learning, machine learning, and deep learning models.

Background

Methods

OmicsDI is part of the ELIXIR infrastructure

Tweets

Forecasting CO₂ emissions of fuel vehicles for an ecological world using ensemble learning, machine learning, and deep learning models.