International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
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 Volume 12, Issue 4 (April 2025), Pages: 34-43

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 Original Research Paper

Enhancing global methane emissions forecasting using hybrid time series models

 Author(s): 

 Maryam Habadi *, Mona Alshehri, Ibtesam Alsaggaf

 Affiliation(s):

 Department of Statistics, King Abdulaziz University, Jeddah, Saudi Arabia

 Full text

  Full Text - PDF

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-6392-1697

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2025.04.005

 Abstract

Global warming is a major environmental issue that raises the average air temperature on Earth's surface. Human activities have played a key role in increasing greenhouse gas emissions, which contribute to higher temperatures and climate change. Methane is the second most significant greenhouse gas driving global warming. This study focuses on predicting global methane emissions using the SARIMA (Seasonal Autoregressive Integrated Moving Average) statistical model and three machine learning models: MLP (Multilayer Perceptron), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit). Two hybrid models, SARIMA-MLP and SARIMA-GRU, were also applied. The models’ accuracy was assessed using statistical metrics, including Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings show that the SARIMA model outperformed the standalone machine learning models. However, the hybrid models demonstrated better forecasting performance, with SARIMA-GRU emerging as the most effective model for predicting global methane emissions. The forecast results indicate a continuous rise in methane emissions over time.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Global warming, Methane emissions, Forecasting models, Hybrid models, Climate change

 Article history

 Received 4 September 2024, Received in revised form 6 January 2025, Accepted 8 April 2025

 Acknowledgment

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Habadi M, Alshehri M, and Alsaggaf I (2025). Enhancing global methane emissions forecasting using hybrid time series models. International Journal of Advanced and Applied Sciences, 12(4): 34-43

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 

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