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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10255431" target="_blank" >RIV/61989100:27240/24:10255431 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27730/24:10255431

Výsledek na webu

<a href="https://www.tandfonline.com/doi/full/10.1080/19942060.2024.2391988" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/19942060.2024.2391988</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/19942060.2024.2391988" target="_blank" >10.1080/19942060.2024.2391988</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 region. The study uses historical carbon dioxide emissions data, socioeconomic indicators such as GDP, population density, energy consumption, and urbanisation rates, and environmental indicators such as temperature, precipitation, and forest cover. Through comprehensive experimentation, the study evaluates the performance of each combination, revealing varying effectiveness levels. The MFO-MLP combination achieved the highest accuracy with R2 values of 0.9996 and 0.9995 and RMSE values of 11.7065 and 12.8890 for the training and testing datasets, respectively. The GOA-MLP configuration achieved R2 values of 0.9994 and 0.99934 and RMSE values of 15.01306 and 14.59333. The SHO-MLP combination, while effective, showed lower performance with R2 values of 0.9915 and 0.9946 and RMSE values of 55.4516 and 41.575. The findings suggest hybrid techniques can significantly enhance prediction accuracy compared to conventional methods. This research provides valuable insights for policymakers and stakeholders, indicating that optimised machine learning models can support more informed and effective environmental policy-making and sustainability efforts in the OECD-Asia and Oceania region. Future research should explore additional optimisation algorithms and ensemble techniques to improve prediction robustness and accuracy. These findings offer a robust tool for policymakers to forecast emissions more accurately, aiding in developing targeted strategies to reduce carbon footprints and achieve climate goals. (C) 2024 The Author(s). Published by Informa UK Limited, trading as Taylor &amp; Francis Group.

Název v anglickém jazyce

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

Popis výsledku anglicky

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 region. The study uses historical carbon dioxide emissions data, socioeconomic indicators such as GDP, population density, energy consumption, and urbanisation rates, and environmental indicators such as temperature, precipitation, and forest cover. Through comprehensive experimentation, the study evaluates the performance of each combination, revealing varying effectiveness levels. The MFO-MLP combination achieved the highest accuracy with R2 values of 0.9996 and 0.9995 and RMSE values of 11.7065 and 12.8890 for the training and testing datasets, respectively. The GOA-MLP configuration achieved R2 values of 0.9994 and 0.99934 and RMSE values of 15.01306 and 14.59333. The SHO-MLP combination, while effective, showed lower performance with R2 values of 0.9915 and 0.9946 and RMSE values of 55.4516 and 41.575. The findings suggest hybrid techniques can significantly enhance prediction accuracy compared to conventional methods. This research provides valuable insights for policymakers and stakeholders, indicating that optimised machine learning models can support more informed and effective environmental policy-making and sustainability efforts in the OECD-Asia and Oceania region. Future research should explore additional optimisation algorithms and ensemble techniques to improve prediction robustness and accuracy. These findings offer a robust tool for policymakers to forecast emissions more accurately, aiding in developing targeted strategies to reduce carbon footprints and achieve climate goals. (C) 2024 The Author(s). Published by Informa UK Limited, trading as Taylor &amp; Francis Group.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Engineering Applications of Computational Fluid Mechanics

ISSN

1994-2060

e-ISSN

—

Svazek periodika

18

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

26

Strana od-do

—

Kód UT WoS článku

001297452700001

EID výsledku v databázi Scopus

2-s2.0-85202196193