Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT) (NRF-2022M2B5A1080696).
References
- S.D. Patil, Y. Gu, F.S. A Dias, M. Stieglitz, G. Turk, Predicting the spectral information of future land cover using machine learning, Int. J. Rem. Sens. 38 (20) (2017) 5592-5607. https://doi.org/10.1080/01431161.2017.1343512
- I. Tirkel, G. Rabinowitz, D. Price, D. Sutherland, Wafer fabrication yield learning and cost analysis based on in-line inspection, Int. J. Prod. Res. 54 (12) (2016) 3578-3590. https://doi.org/10.1080/00207543.2015.1106609
- S.M.A. Zaidi, V. Chandola, M.R. Allen, J. Sanyal, R.N. Stewart, B.L. Bhaduri, R. A. McManamay, Machine learning for energy-water nexus:challenges and opportunities, Big Earth Data 2 (3) (2018) 228-267. https://doi.org/10.1080/20964471.2018.1526057
- IEA, IEA World Energy Outlook 2018, International Energy Agency (IEA), Paris, 2019. France, https://www.iea.org/weo/.
- T.H. Woo, Social selection analysis for a role of nuclear power generation by evolutionary game theory (EGT) in the aspect of global warming assessment, Int. J. Glob. Warming 20 (1) (2020) 25-36. https://doi.org/10.1504/IJGW.2020.10026374
- S. Ali, J. Jiang, S.T. Hassan, A.A. Shah, Revolution of nuclear energy efficiency, economic complexity, air transportation and industrial improvement on environmental footprint cost, Nucl. Eng. Technol. 54 (10) (2022) 3682-3694. https://doi.org/10.1016/j.net.2022.05.022
- I. Khan Bilal, D. Tan, W. Azam, S.T. Hassan, Alternate energy sources and environmental quality: the impact of inflation dynamics, Gondwana Res. 106 (2022) 51-63. https://doi.org/10.1016/j.gr.2021.12.011
- S.T. Hassan, P. Wang, I. Khan, B. Zhu, The impact of economic complexity, technology advancements, and nuclear energy consumption on the ecological footprint of the USA: towards circular economy initiatives, Gondwana Res. 113 (2023) 237-246.
- S.T. Hassan, B. Batool, P. Wang, B. Zhu, M. Sadiq, Impact of economic complexity index, globalization, and nuclear energy consumption on ecological footprint: first insights in OECD context, Energy 263 (2023), 125628. Part A.
- M. Sadiq, R. Shinwari, F. Wen, M. Usman, S.T. Hassan, F. Taghizadeh-Hesary, Do globalization and nuclear energy intensify the environmental costs in top nuclear energy-consuming countries? Prog. Nucl. Energy 156 (2023), 104533.
- M.T. Kartal, U.K. Pata, S.K. Depren, O. Depren, Effects of possible changes in natural gas, nuclear, and coal energy consumption on CO2 emissions: evidence from France under Russia's gas supply cuts by dynamic ARDL simulations approach, Appl. Energy 339 (2023), 120983.
- U.K. Pata, A. Samour, Do renewable and nuclear energy enhance environmental quality in France? A new EKC approach with the load capacity factor, Prog. Nucl. Energy 149 (2022), 104249.
- U.K. Pata, M.T. Kartal, Impact of nuclear and renewable energy sources on environment quality: testing the EKC and LCC hypotheses for South Korea, Nucl. Eng. Technol. 55 (2) (2023) 587-594. https://doi.org/10.1016/j.net.2022.10.027
- U.K. Pata, M.T. Kartal, S. Erdogan, S.A. Sarkodie, The role of renewable and nuclear energy R&D expenditures and income on environmental quality in Germany: scrutinizing the EKC and LCC hypotheses with smooth structural changes, Appl. Energy 342 (2023), 121138.
- X. Jin, Z. Ahmed, U.K. Pata, M.T. Kartal, S. Erdogan, Do investments in green energy, energy efficiency, and nuclear energy R&D improve the load capacity factor? An augmented ARDL approach, Geosci. Front. (2023), 101646 (In Press).
- M. Amozegar, K. Khorasani, An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines, Neural Network. 76 (2016) 106-121. https://doi.org/10.1016/j.neunet.2016.01.003
- B. Wilsom, The Machine Learning Dictionary, 2012. http://www.cse.unsw.edu.au/~billw/mldict.html#activnfn.
- Wikipedia, Artificial Neural Network, 2015. https://en.wikipedia.org/wiki/Artificial_neural_network#cite_note-30.
- F.C. Jelen, J.H. Black, Cost and Optimization Engineering, third ed., McGraw-Hill Book Company, New York, USA, 1983.
- T.H. Yeh, S. Deng, Application of machine learning methods to cost estimation of product life cycle, Int. J. Comput. Integrated Manuf. 25 (4-5) (2012) 340-352. https://doi.org/10.1080/0951192X.2011.645381
- Ventana, Vensim Software, 2015. Harvard, USA, https://vensim.com.
- T.H. Woo, Global warming analysis for greenhouse gases impacts comparable to carbon-free nuclear energy using neuro-fuzzy algorithm, Int. J. Glob. Warming 17 (2) (2019) 219-233. https://doi.org/10.1504/IJGW.2019.097862