Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning

Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning

This paper investigates the application of three nature-inspired optimisation algorithms – SHO, MFO, and GOA – combined with four machine learning methods – Gaussian Processes, Linear Regression, MLP, and Random Forest – to enhance carbon dioxide emission prediction in the OECD – Asia and Oceania re...

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Bibliographic Details
Main Authors: Loke Kok Foong, Vojtech Blazek, Lukas Prokop, Stanislav Misak, Farruh Atamurotov, Nima Khalilpoor
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:Engineering Applications of Computational Fluid Mechanics
Subjects:
CO2 emission
environmental policy
metaheuristic algorithm
OECD
Online Access:https://www.tandfonline.com/doi/10.1080/19942060.2024.2391988
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https://www.tandfonline.com/doi/10.1080/19942060.2024.2391988

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