气候变化领域集成服务门户(CCPortal): Carbon Emission Prediction Through the Harmonization of Extreme Learning Machine and INFO Algorithm

CCPortal

DOI	10.1109/ACCESS.2024.3390408
	Carbon Emission Prediction Through the Harmonization of Extreme Learning Machine and INFO Algorithm
	Feda, Afi Kekeli; Adegboye, Oluwatayomi Rereloluwa; Agyekum, Ephraim Bonah; Shuaibu Hassan, Abdurrahman; Kamel, Salah
发表日期	2024
ISSN	2169-3536
起始页码	12
卷号	12
英文摘要	This research introduces a novel optimization algorithm, weIghted meaN oF vectOrs (INFO), integrated with the Extreme Learning Machine (ELM) to enhance the predictive capabilities of the model for carbon dioxide (CO2) emissions. INFO optimizes ELM's weight and bias. In six classic test problems and CEC 2019 functions, INFO demonstrated notable strengths in achieving optimal solutions for various functions. The proposed hybrid model, ELM-INFO, exhibits superior performance in forecasting CO2 emissions, as substantiated by rigorous evaluation metrics. Notably, it achieves a superior R2 value of 0.9742, alongside minimal values in Root Mean Squared Error (RMSE) at 0.01937, Mean Squared Error (MSE) at 0.00037, Mean Absolute Error (MAE) at 0.0136, and Mean Absolute Percentage Error (MAPE) at 0.0060. These outcomes underscore the robustness of ELM-INFO in accurately predicting CO2 emissions within the testing dataset. Additionally, economic growth is the most significant element, as indicated by ELM-INFO's permutation significance analysis, which causes the model's MSE to increase by 19%. Trade openness and technological innovation come next, each adding 7.6% and 8.1% to the model's MSE increase, respectively. According to ELM-INFO's performance, it's a powerful tool for developing ecologically sound policies that improve environmental resilience and sustainability.
英文关键词	Artificial neural network; carbon emission prediction; convergence acceleration; extreme learning machine; metaheuristic algorithms
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001214261900001
来源期刊	IEEE ACCESS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/295229
作者单位	Lefke Avrupa University; Ural Federal University; Egyptian Knowledge Bank (EKB); Aswan University
推荐引用方式 GB/T 7714	Feda, Afi Kekeli,Adegboye, Oluwatayomi Rereloluwa,Agyekum, Ephraim Bonah,et al. Carbon Emission Prediction Through the Harmonization of Extreme Learning Machine and INFO Algorithm[J],2024,12.
APA	Feda, Afi Kekeli,Adegboye, Oluwatayomi Rereloluwa,Agyekum, Ephraim Bonah,Shuaibu Hassan, Abdurrahman,&Kamel, Salah.(2024).Carbon Emission Prediction Through the Harmonization of Extreme Learning Machine and INFO Algorithm.IEEE ACCESS,12.
MLA	Feda, Afi Kekeli,et al."Carbon Emission Prediction Through the Harmonization of Extreme Learning Machine and INFO Algorithm".IEEE ACCESS 12(2024).