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Forecasting the annual carbon dioxide emissions of Malaysia using Lasso-GMDH neural network-based

In this study, it was intended to develop an accurate forecasting model for the annually CO2 emission of Malaysia in the short-term. For this purpose, the Group Method of Data Handling (GMDH) model as one of the Neural Networks (NNs) was utilized to structure a nonlinear time-series based forecastin...

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Main Author: Shabri, Ani
Format: Conference or Workshop Item
Published: 2022
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/98756/
http://dx.doi.org/10.1109/ISCAIE54458.2022.9794541
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spelling my.utm.987562023-02-02T08:24:15Z http://eprints.utm.my/id/eprint/98756/ Forecasting the annual carbon dioxide emissions of Malaysia using Lasso-GMDH neural network-based Shabri, Ani QA Mathematics In this study, it was intended to develop an accurate forecasting model for the annually CO2 emission of Malaysia in the short-term. For this purpose, the Group Method of Data Handling (GMDH) model as one of the Neural Networks (NNs) was utilized to structure a nonlinear time-series based forecasting model. In order to improve GMDH prediction accuracy, this paper highlights the drawbacks of using the least square method to solve model parameters and attempts to use the Lasso method (Lasso-GMDH). A case study with the proposed model was carried out for one-year-ahead forecasting of CO2 emissions data during the years 2000-2016. Three different models: grey model GM(1,N), artificial neural network (ANN) and GMDH models were investigated to model the Co2 emission forecast. The comparison revealed that Lasso-GMDH model has the highest general performance for forecasting the annually CO2 emission of Malaysia. 2022 Conference or Workshop Item PeerReviewed Shabri, Ani (2022) Forecasting the annual carbon dioxide emissions of Malaysia using Lasso-GMDH neural network-based. In: 12th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2022, 21 - 22 May 2022, Virtual, Online. http://dx.doi.org/10.1109/ISCAIE54458.2022.9794541
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA Mathematics
spellingShingle QA Mathematics
Shabri, Ani
Forecasting the annual carbon dioxide emissions of Malaysia using Lasso-GMDH neural network-based
description In this study, it was intended to develop an accurate forecasting model for the annually CO2 emission of Malaysia in the short-term. For this purpose, the Group Method of Data Handling (GMDH) model as one of the Neural Networks (NNs) was utilized to structure a nonlinear time-series based forecasting model. In order to improve GMDH prediction accuracy, this paper highlights the drawbacks of using the least square method to solve model parameters and attempts to use the Lasso method (Lasso-GMDH). A case study with the proposed model was carried out for one-year-ahead forecasting of CO2 emissions data during the years 2000-2016. Three different models: grey model GM(1,N), artificial neural network (ANN) and GMDH models were investigated to model the Co2 emission forecast. The comparison revealed that Lasso-GMDH model has the highest general performance for forecasting the annually CO2 emission of Malaysia.
format Conference or Workshop Item
author Shabri, Ani
author_facet Shabri, Ani
author_sort Shabri, Ani
title Forecasting the annual carbon dioxide emissions of Malaysia using Lasso-GMDH neural network-based
title_short Forecasting the annual carbon dioxide emissions of Malaysia using Lasso-GMDH neural network-based
title_full Forecasting the annual carbon dioxide emissions of Malaysia using Lasso-GMDH neural network-based
title_fullStr Forecasting the annual carbon dioxide emissions of Malaysia using Lasso-GMDH neural network-based
title_full_unstemmed Forecasting the annual carbon dioxide emissions of Malaysia using Lasso-GMDH neural network-based
title_sort forecasting the annual carbon dioxide emissions of malaysia using lasso-gmdh neural network-based
publishDate 2022
url http://eprints.utm.my/id/eprint/98756/
http://dx.doi.org/10.1109/ISCAIE54458.2022.9794541
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score 13.214268

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