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

Carbon dioxide (CO۲) is the main greenhouse gas responsible for global warming. Early prediction of CO۲ 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 CO۲ 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 CO۲concentrations, which contributes to the fundamental knowledge of the environmental engineering perspective. Doi: ۱۰.۲۸۹۹۱/CEJ-۲۰۲۳-۰۹-۰۴-۰۴ Full Text: PDF

Carbone Dioxide; Greenhouse Gas; Climate Change; Complete Orthogonal Decomposition.