TY - JOUR

T1 - Carbon Emission Prediction Through the Harmonization of Extreme Learning Machine and INFO Algorithm

AU - Feda, Afi Kekeli

AU - Adegboye, Oluwatayomi Rereloluwa

AU - Agyekum, Ephraim Bonah

AU - Shuaibu Hassan, Abdurrahman

AU - Kamel, Salah

PY - 2024/1/1

Y1 - 2024/1/1

N2 - 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.

AB - 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.

UR - https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=tsmetrics&SrcApp=tsm_test&DestApp=WOS_CPL&DestLinkType=FullRecord&KeyUT=001214261900001

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85190749403

U2 - 10.1109/ACCESS.2024.3390408

DO - 10.1109/ACCESS.2024.3390408

M3 - Article

VL - 12

SP - 60310

EP - 60328

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -