Short-, Medium-, and Long-Term Prediction of Carbon Dioxide Emissions using Wavelet-Enhanced Extreme Learning Machine

Mohamed Khalid AlOmar; Mohammed Majeed Hameed; Nadhir Al-Ansari; Siti Fatin Mohd  Razali; Mohammed Abdulhakim AlSaadi

doi:10.28991/CEJ-2023-09-04-04

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Mohamed Khalid AlOmar
mohd.alomar@yahoo.com
Department of Civil Engineering, Al-Maarif University College, 31001 Ramadi,, Iraq
Mohammed Majeed Hameed 1) Department of Civil Engineering, Al-Maarif University College, 31001 Ramadi, Iraq. 2) Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM),43600 Bangi, Selangor,, Malaysia
Nadhir Al-Ansari Civil Engineering Department, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea,, Sweden
Siti Fatin Mohd Razali Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM),43600 Bangi, Selangor,, Malaysia
Mohammed Abdulhakim AlSaadi Natural and Medical Sciences Research Center (NMSRC), University of Nizwa Sultanate of Oman, Nizwa,, Oman

Vol. 9 No. 4 (2023): April

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Carbon dioxide (CO₂) is the main greenhouse gas responsible for global warming. Early prediction of CO₂ is critical for developing strategies to mitigate the effects of climate change. A sophisticated version of the extreme learning machine (ELM), the wavelet enhanced extreme learning machine (W-EELM), is used to predict CO₂ on different time scales (weekly, monthly, and yearly). Data were collected from the Mauna Loa Observatory station in Hawaii, which is ideal for global air sampling. Instead of the traditional method (singular value decomposition), a complete orthogonal decomposition (COD) was used to accurately calculate the weights of the ELM output layers. Another contribution of this study is the removal of noise from the input signal using the wavelet transform technique. The results of the W-EELM model are compared with the results of the classical ELM. Various statistical metrics are used to evaluate the models, and the comparative figures confirm the superiority of the applied models over the ELM model. The proposed W-EELM model proves to be a robust and applicable computer-based technology for modeling CO₂concentrations, which contributes to the fundamental knowledge of the environmental engineering perspective.

Doi: 10.28991/CEJ-2023-09-04-04

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AlOmar, M. K., Hameed, M. M., Al-Ansari, N., Mohd Razali, S. F., & AlSaadi, M. A. (2023). Short-, Medium-, and Long-Term Prediction of Carbon Dioxide Emissions using Wavelet-Enhanced Extreme Learning Machine. Civil Engineering Journal, 9(4), 815–834. https://doi.org/10.28991/CEJ-2023-09-04-04

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Publication Start Year:	2015
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Short-, Medium-, and Long-Term Prediction of Carbon Dioxide Emissions using Wavelet-Enhanced Extreme Learning Machine

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