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Forecasting CO2 emissions in Malaysia using Group Method of Data Handling / Basri Badyalina ... [et al.]

Critical environmental issues, such as climate change, pollution, and resource depletion, urgently require data-driven decision-making and accurate forecasting to guide sustainable policies and interventions. However, forecasting is a complex task that necessitates rigorous research to ensure precis...

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Main Authors: Badyalina, Basri, Ya’acob, Fatin Farazh, Alpandi, Rabiatul Munirah, Zamani, Nur Diana, Zainoddin, Amir Imran, Abd Jalal, Muhammad Zulqarnain Hakim
Format: Article
Language:English
Published: Universiti Teknologi MARA, Perak 2024
Subjects:
QA Mathematics
Electronic Computers. Computer Science
Online Access:https://ir.uitm.edu.my/id/eprint/97625/1/97625.pdf
https://ir.uitm.edu.my/id/eprint/97625/
https://mijuitm.com.my/
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spelling my.uitm.ir.976252024-07-19T03:37:13Z https://ir.uitm.edu.my/id/eprint/97625/ Forecasting CO2 emissions in Malaysia using Group Method of Data Handling / Basri Badyalina ... [et al.] msij Badyalina, Basri Ya’acob, Fatin Farazh Alpandi, Rabiatul Munirah Zamani, Nur Diana Zainoddin, Amir Imran Abd Jalal, Muhammad Zulqarnain Hakim QA Mathematics Electronic Computers. Computer Science Critical environmental issues, such as climate change, pollution, and resource depletion, urgently require data-driven decision-making and accurate forecasting to guide sustainable policies and interventions. However, forecasting is a complex task that necessitates rigorous research to ensure precise predictions essential for addressing these environmental challenges effectively. To meet these forecasting challenges, this study utilized Group Method of Data Handling (GMDH) method, focusing on CO2 emission in Malaysia. The analysis of the GMDH forecasting model for CO2 emission provides distinct patterns in the behaviors of three input variables X1, defined as (yt−2,yt−3,yt−5), X2 defined as (yt−1,yt−5,yt−6,yt−7) and X3 defined as (yt−2,yt−5,yt−6,yt−8,yt−10). Notably, X2 consistently exhibits strong performance, whereas X1 and X3 face difficulties, particularly in the forecasting phase. The GMDH model demonstrates proficiency in adaptive self-organization, automatic feature extraction, managing non-linear relationships, and interpretability, enhancing its effectiveness in capturing complex patterns in CO2 emission data. The observed decrease in performance for specific inputs during the forecasting process highlights the necessity for improving and adjusting the model and developing a detailed grasp of the dynamics of the variables involved. Universiti Teknologi MARA, Perak 2024-05 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/97625/1/97625.pdf Forecasting CO2 emissions in Malaysia using Group Method of Data Handling / Basri Badyalina ... [et al.]. (2024) Mathematical Sciences and Informatics Journal (MIJ) <https://ir.uitm.edu.my/view/publication/Mathematical_Sciences_and_Informatics_Journal_=28MIJ=29/>, 5 (1). pp. 10-20. ISSN 2735-0703 https://mijuitm.com.my/
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic QA Mathematics
Electronic Computers. Computer Science
spellingShingle QA Mathematics
Electronic Computers. Computer Science
Badyalina, Basri
Ya’acob, Fatin Farazh
Alpandi, Rabiatul Munirah
Zamani, Nur Diana
Zainoddin, Amir Imran
Abd Jalal, Muhammad Zulqarnain Hakim
Forecasting CO2 emissions in Malaysia using Group Method of Data Handling / Basri Badyalina ... [et al.]
description Critical environmental issues, such as climate change, pollution, and resource depletion, urgently require data-driven decision-making and accurate forecasting to guide sustainable policies and interventions. However, forecasting is a complex task that necessitates rigorous research to ensure precise predictions essential for addressing these environmental challenges effectively. To meet these forecasting challenges, this study utilized Group Method of Data Handling (GMDH) method, focusing on CO2 emission in Malaysia. The analysis of the GMDH forecasting model for CO2 emission provides distinct patterns in the behaviors of three input variables X1, defined as (yt−2,yt−3,yt−5), X2 defined as (yt−1,yt−5,yt−6,yt−7) and X3 defined as (yt−2,yt−5,yt−6,yt−8,yt−10). Notably, X2 consistently exhibits strong performance, whereas X1 and X3 face difficulties, particularly in the forecasting phase. The GMDH model demonstrates proficiency in adaptive self-organization, automatic feature extraction, managing non-linear relationships, and interpretability, enhancing its effectiveness in capturing complex patterns in CO2 emission data. The observed decrease in performance for specific inputs during the forecasting process highlights the necessity for improving and adjusting the model and developing a detailed grasp of the dynamics of the variables involved.
format Article
author Badyalina, Basri
Ya’acob, Fatin Farazh
Alpandi, Rabiatul Munirah
Zamani, Nur Diana
Zainoddin, Amir Imran
Abd Jalal, Muhammad Zulqarnain Hakim
author_facet Badyalina, Basri
Ya’acob, Fatin Farazh
Alpandi, Rabiatul Munirah
Zamani, Nur Diana
Zainoddin, Amir Imran
Abd Jalal, Muhammad Zulqarnain Hakim
author_sort Badyalina, Basri
title Forecasting CO2 emissions in Malaysia using Group Method of Data Handling / Basri Badyalina ... [et al.]
title_short Forecasting CO2 emissions in Malaysia using Group Method of Data Handling / Basri Badyalina ... [et al.]
title_full Forecasting CO2 emissions in Malaysia using Group Method of Data Handling / Basri Badyalina ... [et al.]
title_fullStr Forecasting CO2 emissions in Malaysia using Group Method of Data Handling / Basri Badyalina ... [et al.]
title_full_unstemmed Forecasting CO2 emissions in Malaysia using Group Method of Data Handling / Basri Badyalina ... [et al.]
title_sort forecasting co2 emissions in malaysia using group method of data handling / basri badyalina ... [et al.]
publisher Universiti Teknologi MARA, Perak
publishDate 2024
url https://ir.uitm.edu.my/id/eprint/97625/1/97625.pdf
https://ir.uitm.edu.my/id/eprint/97625/
https://mijuitm.com.my/
_version_ 1806422095067873280
score 13.211869

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