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

IF 5.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Engineering Applications of Computational Fluid Mechanics Pub Date : 2024-08-23 DOI:10.1080/19942060.2024.2391988

Loke Kok Foong, Vojtech Blazek, Lukas Prokop, Stanislav Misak, Farruh Atamurotov, Nima Khalilpoor

引用次数: 0

Abstract

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, M...

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改进亚洲和大洋洲（经合组织）的二氧化碳排放预测：自然启发优化算法与传统机器学习的比较

本文研究了三种自然启发优化算法（SHO、MFO 和 GOA）与四种机器学习方法（高斯过程、线性回归、M...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Engineering Applications of Computational Fluid Mechanics ENGINEERING, MULTIDISCIPLINARY-ENGINEERING, MECHANICAL

CiteScore

10.60

自引率

14.80%

发文量

109

审稿时长

3.4 months

期刊介绍： The aim of Engineering Applications of Computational Fluid Mechanics is a continuous and timely dissemination of innovative, practical and industrial applications of computational techniques to solve the whole range of hitherto intractable fluid mechanics problems. The journal is a truly interdisciplinary forum and publishes original contributions on the latest advances in numerical methods in fluid mechanics and their applications to various engineering fields including aeronautic, civil, environmental, hydraulic and mechanical. The journal has a distinctive and balanced international contribution, with emphasis on papers addressing practical problem-solving by means of robust numerical techniques to generate precise flow prediction and optimum design, and those fostering the thorough understanding of the physics of fluid motion. It is an open access journal.