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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

Abstract


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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Keywords


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

References


Qiao, W., Lu, H., Zhou, G., Azimi, M., Yang, Q., & Tian, W. (2020). A hybrid algorithm for carbon dioxide emissions forecasting based on improved lion swarm optimizer. Journal of Cleaner Production, 244, 118612. doi:10.1016/j.jclepro.2019.118612.

Acheampong, A. O., & Boateng, E. B. (2019). Modelling carbon emission intensity: Application of artificial neural network. Journal of Cleaner Production, 225, 833–856. doi:10.1016/j.jclepro.2019.03.352.

Li, F., Zhang, J., Oko, E., & Wang, M. (2017). Modelling of a post-combustion CO2 capture process using extreme learning machine. International Journal of Coal Science and Technology, 4(1), 33–40. doi:10.1007/s40789-017-0158-1.

Alias, N. I., Jayakumar, J. K., & Zain, S. M. (2018). Characterization of waste cooking oil for biodiesel production. Jurnal Kejuruteraan, 1(2), 79-83. doi:10.17576/jkukm-2018-si1(2)-10.

Ridzuan, N. H. A. M., Marwan, N. F., Khalid, N., Ali, M. H., & Tseng, M. L. (2020). Effects of agriculture, renewable energy, and economic growth on carbon dioxide emissions: Evidence of the environmental Kuznets curve. Resources, Conservation and Recycling, 160, 104879. doi:10.1016/j.resconrec.2020.104879.

Marjanović, V., Milovančević, M., & Mladenović, I. (2016). Prediction of GDP growth rate based on carbon dioxide (CO2) emissions. Journal of CO2 Utilization, 16, 212–217. doi:10.1016/j.jcou.2016.07.009.

Yatim, P., Mamat, M. N., Mohamad-Zailani, S. H., & Ramlee, S. (2016). Energy policy shifts towards sustainable energy future for Malaysia. Clean Technologies and Environmental Policy, 18(6), 1685–1695. doi:10.1007/s10098-016-1151-x.

Acheampong, A. O. (2018). Economic growth, CO2 emissions and energy consumption: What causes what and where? Energy Economics, 74, 677–692. doi:10.1016/j.eneco.2018.07.022.

Yaacob, N. F. F., Yazid, M. R. M., Maulud, K. N. A., & Basri, N. E. A. (2020). A review of the measurement method, analysis and implementation policy of carbon dioxide emission from transportation. Sustainability (Switzerland), 12(14), 5873. doi:10.3390/su12145873.

An, Q., Wen, Y., Xiong, B., Yang, M., & Chen, X. (2017). Allocation of carbon dioxide emission permits with the minimum cost for Chinese provinces in big data environment. Journal of Cleaner Production, 142, 886–893. doi:10.1016/j.jclepro.2016.07.072.

Foster, V., & Bedrosyan, D. (2014). Understanding CO2 emissions from the global energy sector. Live Wire, 2014/5, World Bank, Washington, United States.

IEA. (2020). Global CO2 emissions in 2019. International Energy Agency (IEA), Paris, France. Available online: https://www.iea.org/articles/global-co2-emissions-in-2019 (accessed on March 2023).

Gielen, D., Boshell, F., Saygin, D., Bazilian, M. D., Wagner, N., & Gorini, R. (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews, 24, 38–50. doi:10.1016/j.esr.2019.01.006.

Lu, H., Huang, K., Azimi, M., & Guo, L. (2019). Blockchain technology in the oil and gas industry: A review of applications, opportunities, challenges, and risks. IEEE Access, 7, 41426–41444. doi:10.1109/ACCESS.2019.2907695.

Reijnders, L., & Huijbregts, M. A. J. (2008). Palm oil and the emission of carbon-based greenhouse gases. Journal of Cleaner Production, 16(4), 477–482. doi:10.1016/j.jclepro.2006.07.054.

Zhang, Y., & Fu, B. (2023). Impact of China's establishment of ecological civilization pilot zones on carbon dioxide emissions. Journal of Environmental Management, 325, 116652. doi:10.1016/j.jenvman.2022.116652.

Baareh, A. K. (2013). Solving the Carbon Dioxide Emission Estimation Problem: An Artificial Neural Network Model. Journal of Software Engineering and Applications, 06(07), 338–342. doi:10.4236/jsea.2013.67042.

Fang, D., Zhang, X., Yu, Q., Jin, T. C., & Tian, L. (2018). A novel method for carbon dioxide emission forecasting based on improved Gaussian processes regression. Journal of Cleaner Production, 173, 143–150. doi:10.1016/j.jclepro.2017.05.102.

Lin, B., & Ahmad, I. (2017). Analysis of energy related carbon dioxide emission and reduction potential in Pakistan. Journal of Cleaner Production, 143, 278–287. doi:10.1016/j.jclepro.2016.12.113.

Oh, I., Wehrmeyer, W., & Mulugetta, Y. (2010). Decomposition analysis and mitigation strategies of CO2 emissions from energy consumption in South Korea. Energy Policy, 38(1), 364–377. doi:10.1016/j.enpol.2009.09.027.

Mardani, A., Liao, H., Nilashi, M., Alrasheedi, M., & Cavallaro, F. (2020). A multi-stage method to predict carbon dioxide emissions using dimensionality reduction, clustering, and machine learning techniques. Journal of Cleaner Production, 275, 122942. doi:10.1016/j.jclepro.2020.122942.

Saleh, C., Dzakiyullah, N. R., & Nugroho, J. B. (2016). Carbon dioxide emission prediction using support vector machine. IOP Conference Series: Materials Science and Engineering, 114, 012148. doi:10.1088/1757-899x/114/1/012148.

Yaseen, Z. M., Sulaiman, S. O., Deo, R. C., & Chau, K. W. (2019). An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction. Journal of Hydrology, 569, 387–408. doi:10.1016/j.jhydrol.2018.11.069.

Shao, Z., Zhai, Q., Wu, J., & Guan, X. (2021). Data Based Linear Power Flow Model: Investigation of a Least-Squares Based Approximation. IEEE Transactions on Power Systems, 36(5), 4246–4258. doi:10.1109/TPWRS.2021.3062359.

Global Monitoring Laboratory. (2023). U.S. Department of Commerce, National Oceanic & Atmospheric Administration, NOAA Research, Boulder, United States. Available online: https://gml.noaa.gov/obop/mlo/aboutus/siteInformation/ mlosite.html (accessed on March 2023).

G.M. Laboratory (2020). Earth System Research Laboratories. U.S. Department of Commerce, National Oceanic & Atmospheric Administration, NOAA Research, Boulder, United States. Available online: https://gml.noaa.gov/ (accessed on March 2023).

Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1–3), 489–501. doi:10.1016/j.neucom.2005.12.126.

Wang, W., Peng, W., Xu, J., Zhang, R., & Zhao, Y. (2018). Driving factor analysis and forecasting of CO2 emissions from power output in China using scenario analysis and CSCWOA-ELM method. Energies, 11(10), 2709. doi:10.3390/en11102709.

Masmoudi, S., Elghazel, H., Taieb, D., Yazar, O., & Kallel, A. (2020). A machine-learning framework for predicting multiple air pollutants’ concentrations via multi-target regression and feature selection. Science of the Total Environment, 715, 136991. doi:10.1016/j.scitotenv.2020.136991.

Wei, S., Yuwei, W., & Chongchong, Z. (2018). Forecasting CO2 emissions in Hebei, China, through moth-flame optimization based on the random forest and extreme learning machine. Environmental Science and Pollution Research, 25(29), 28985–28997. doi:10.1007/s11356-018-2738-z.

Hameed, M. M., Alomar, M. K., Khaleel, F., & Al-Ansari, N. (2021). An Extra Tree Regression Model for Discharge Coefficient Prediction: Novel, Practical Applications in the Hydraulic Sector and Future Research Directions. Mathematical Problems in Engineering, 2021, 7001710. doi:10.1155/2021/7001710.

Hameed, M. M., Alomar, M. K., Mohd Razali, S. F., Kareem Khalaf, M. A., Baniya, W. J., Sharafati, A., & Alsaadi, M. A. (2021). Application of Artificial Intelligence Models for Evapotranspiration Prediction along the Southern Coast of Turkey. Complexity, 2021, 8850243. doi:10.1155/2021/8850243.

Hameed, M. M., AlOmar, M. K., Baniya, W. J., & AlSaadi, M. A. (2022). Prediction of high-strength concrete: high-order response surface methodology modeling approach. Engineering with Computers, 38(2), 1655–1668. doi:10.1007/s00366-021-01284-z.

Hough, P. D., & Vavasis, S. A. (1997). Complete orthogonal decomposition for weighted least squares. SIAM Journal on Matrix Analysis and Applications, 18(2), 369–392. doi:10.1137/S089547989528079X.

Guo, W., Xu, T., & Lu, Z. (2016). An integrated chaotic time series prediction model based on efficient extreme learning machine and differential evolution. Neural Computing and Applications, 27(4), 883–898. doi:10.1007/s00521-015-1903-2.

Addison, P. S., Murray, K. B., & Watson, J. N. (2001). Wavelet Transform Analysis of Open Channel Wake Flows. Journal of Engineering Mechanics, 127(1), 58–70. doi:10.1061/(asce)0733-9399(2001)127:1(58).

Zhu, H., Hu, W., Jing, Y., Cao, Y., Bi, R., & Yang, W. (2018). Soil organic carbon prediction based on scale-specific relationships with environmental factors by discrete wavelet transform. Geoderma, 330, 9–18. doi:10.1016/j.geoderma.2018.05.029.

Nourani, V., Hosseini Baghanam, A., Adamowski, J., & Kisi, O. (2014). Applications of hybrid wavelet-Artificial Intelligence models in hydrology: A review. Journal of Hydrology, 514, 358–377. doi:10.1016/j.jhydrol.2014.03.057.

Sharghi, E., Nourani, V., Najafi, H., & Molajou, A. (2018). Emotional ANN (EANN) and Wavelet-ANN (WANN) Approaches for Markovian and Seasonal Based Modeling of Rainfall-Runoff Process. Water Resources Management, 32(10), 3441–3456. doi:10.1007/s11269-018-2000-y.

Fosten, J. (2019). CO2 emissions and economic activity: A short-to-medium run perspective. Energy Economics, 83, 415–429. doi:10.1016/j.eneco.2019.07.015.

Pla, C., Cuezva, S., Garcia-Anton, E., Fernandez-Cortes, A., Cañaveras, J. C., Sanchez-Moral, S., & Benavente, D. (2016). Changes in the CO2 dynamics in near-surface cavities under a future warming scenario: Factors and evidence from the field and experimental findings. Science of the Total Environment, 565, 1151–1164. doi:10.1016/j.scitotenv.2016.05.160.

Ma, D., Tan, W., Zhang, Z., Wang, X., Xia, F., & Hu, J. (2018). Recognition of leak CO2 with wavelet analysis based on correlation monitoring between CO2 and O2 in atmosphere. Process Safety and Environmental Protection, 114, 64–78. doi:10.1016/j.psep.2017.12.009.

Oh, Y. Y., Yun, S. T., Yu, S., Kim, H. J., & Jun, S. C. (2018). Characterization of Environmental Drivers Controlling the Baseline of Soil Surface CO2 Flux using Wavelet-based Multiresolution State-Space Model and Wavelet Denoising. Energy Procedia, 154, 157–162. doi:10.1016/j.egypro.2018.11.026.

Ebrahimi, H., & Rajaee, T. (2017). Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine. Global and Planetary Change, 148, 181–191. doi:10.1016/j.gloplacha.2016.11.014.

Nury, A. H., Hasan, K., & Alam, Md. J. B. (2017). Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh. Journal of King Saud University - Science, 29(1), 47–61. doi:10.1016/j.jksus.2015.12.002.

AlOmar, M. K., Hameed, M. M., & AlSaadi, M. A. (2020). Multi hours ahead prediction of surface ozone gas concentration: Robust artificial intelligence approach. Atmospheric Pollution Research, 11(9), 1572–1587. doi:10.1016/j.apr.2020.06.024.

Yan, H., Mou, Y., Xu, X., Du, J., Wang, R., & Liu, P. (2018). A comparative assessment of predicting CH4 adsorption on different activated carbons using generalized regression neural network (GRNN), and adaptive network-based fuzzy inference system (ANFIS). Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 41(16), 1983–1992. doi:10.1080/15567036.2018.1548527.

Metia, S., Ha, Q. P., Duc, H. N., & Scorgie, Y. (2020). Urban air pollution estimation using unscented Kalman filtered inverse modeling with scaled monitoring data. Sustainable Cities and Society, 54, 101970. doi:10.1016/j.scs.2019.101970.

Hameed, M., Sharqi, S. S., Yaseen, Z. M., Afan, H. A., Hussain, A., & Elshafie, A. (2017). Application of artificial intelligence (AI) techniques in water quality index prediction: a case study in tropical region, Malaysia. Neural Computing and Applications, 28(1), 893–905. doi:10.1007/s00521-016-2404-7.

Ali, A., Abdulrahman, A., Garg, S., Maqsood, K., & Murshid, G. (2019). Application of artificial neural networks (ANN) for vapor-liquid-solid equilibrium prediction for CH4-CO2 binary mixture. Greenhouse Gases: Science and Technology, 9(1), 67–78. doi:10.1002/ghg.1833.

Alomar, M. K., Hameed, M. M., Al-Ansari, N., & Alsaadi, M. A. (2020). Data-Driven Model for the Prediction of Total Dissolved Gas: Robust Artificial Intelligence Approach. Advances in Civil Engineering, 2020, 6618842. doi:10.1155/2020/6618842.


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

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Copyright (c) 2023 Mohamed Khalid AlOmar, Mohammed Majeed Hameed, Nadhir Al-Ansari, Siti Fatin Mohd Razali, Mohammed Abdulhakim AlSaadi

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