Data-driven models for atmospheric air temperature forecasting at a continental climate region
pmc-PMC9632800.pdf · Phiên bản 1 · 31 trang
2. Vidmar R. J., ’On the use of atmospheric pressure plasmas as electromagnetic reflectors and absorb- ers’, IEEE Trans. Plasma Sci., vol. 18, no. 4, pp. 733–741, 1990, https://doi.org/10.1109/27.57528 3. Altan Dombaycı O¨ . and Go¨ lcu¨ M., ’Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey’, Renew. Energy, vol. 34, no. 4, pp. 1158–1161, 2009, https:// doi.org/10.1016/j.renene.2008.07.007 4. Li X., Li Z., Huang W., and Zhou P., ’Performance of statistical and machine learning ensembles for daily temperature downscaling’, Theor. Appl. Climatol., vol. 140, no. 1, pp. 571–588, 2020, https://doi. org/10.1007/s00704-020-03098-3 5. Park I., Kim H. S., Lee J., Kim J. H., Song C. H., and Kim H. K., ’Temperature Prediction Using the Miss- ing Data Refinement Model Based on a Long Short-Term Memory Neural Network’, Atmosphere, vol. 10, no. 11. 2019, https://doi.org/10.3390/atmos10110718 6. Sekertekin A., Bilgili M., Arslan N., Yildirim A., Celebi K., and Ozbek A., ’Short-term air temperature pre- diction by adaptive neuro-fuzzy inference system (ANFIS) and long short-term memory (LSTM) net- work’, Meteorol. Atmos. Phys., vol. 133, no. 3, pp. 943–959, 2021, https://doi.org/10.1007/s00703-021- 00791-4 7. Smith B. A., Hoogenboom G., and McClendon R. W., ’Artificial neural networks for automated year- round temperature prediction’, Comput. Electron. Agric., vol. 68, no. 1, pp. 52–61, 2009, https://doi.org/ 10.1016/j.compag.2009.04.003
8. Ozbek A., Sekertekin A., Bilgili M., and Arslan N., ’Prediction of 10-min, hourly, and daily atmospheric air temperature: comparison of LSTM, ANFIS-FCM, and ARMA’, Arab. J. Geosci., vol. 14, no. 7, p. 622, 2021, https://doi.org/10.1007/s12517-021-06982-y 9. S. Zahroh, Y. Hidayat, R. S. Pontoh, A. Santoso, F. Sukono, and A. T. Bon, ’Modeling and forecasting daily temperature in Bandung’, in Proceedings of the International Conference on Industrial Engineering and Operations Management Riyadh, Saudi Arabia, 2019, pp. 406–412. 10. Venkadesh S., Hoogenboom G., Potter W., and McClendon R., ’A genetic algorithm to refine input data selection for air temperature prediction using artificial neural networks’, Appl. Soft Comput., vol. 13, no. 5, pp. 2253–2260, 2013, https://doi.org/10.1016/j.asoc.2013.02.003 11. Zhang X., Zhang Q., Zhang G., Nie Z., Gui Z., and Que H., ’A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition’, International Journal of Environmental Research and Public Health, vol. 15, no. 5. 2018, https://doi.org/10.3390/ijerph15051032 PMID: 29883381 12. Salcedo-Sanz S., Deo R. C., Carro-Calvo L., and Saavedra-Moreno B., ’Monthly prediction of air tem- perature in Australia and New Zealand with machine learning algorithms’, Theor. Appl. Climatol., vol. 125, no. 1, pp. 13–25, 2016, https://doi.org/10.1007/s00704-015-1480-4 13. Kaufmann R. K., Kauppi H., Mann M. L., and Stock J. H., ’Reconciling anthropogenic climate change
with observed temperature 1998–2008’, Proc. Natl. Acad. Sci., vol. 108, no. 29, pp. 11790–11793, 2011. https://doi.org/10.1073/pnas.1102467108 PMID: 21730180 14. Stone D. A. and Allen M. R., ’Attribution of global surface warming without dynamical models’, Geophys. Res. Lett., vol. 32, no. 18, Sep. 2005, https://doi.org/10.1029/2005GL023682 15. Douglass D. H., Blackman E. G., and Knox R. S., ’Temperature response of Earth to the annual solar irradiance cycle’, Phys. Lett. A, vol. 323, no. 3, pp. 315–322, 2004, https://doi.org/10.1016/j.physleta. 2004.01.066 16. Afzali M., Afzali A., and Zahedi G., ’The potential of artificial neural network technique in daily and monthly ambient air temperature prediction’, Int. J. Environ. Sci. Dev., vol. 3, no. 1, p. 33, 2012. 17. M. M. Hameed, F. Khaleel, and D. Khaleel, ’Employing a robust data-driven model to assess the envi- ronmental damages caused by installing grouted columns’, in 2021 Third International Sustainability and Resilience Conference: Climate Change, 2021, pp. 305–309. 18. Hammed M. M., AlOmar M. K., Khaleel F., and Al-Ansari N., ’An Extra Tree Regression Model for Dis- charge Coefficient Prediction: Novel, Practical Applications in the Hydraulic Sector and Future Research Directions’, Math. Probl. Eng., vol. 2021, pp. 1–19, 2021, https://doi.org/10.1155/2021/ 7001710 19. Prodhan F. A. et al., ’Projection of future drought and its impact on simulated crop yield over South Asia using ensemble machine learning approach’, Sci. Total Environ., vol. 807, p. 151029, 2022. https://doi.
org/10.1016/j.scitotenv.2021.151029 PMID: 34673078 20. Aghelpour P., Mohammadi B., Mehdizadeh S., Bahrami-Pichaghchi H., and Duan Z., ’A novel hybrid dragonfly optimization algorithm for agricultural drought prediction’, Stoch. Environ. Res. Risk Assess., vol. 35, no. 12, pp. 2459–2477, 2021, https://doi.org/10.1007/s00477-021-02011-2 21. Banadkooki F. B., Singh V. P., and Ehteram M., ’Multi-timescale drought prediction using new hybrid artificial neural network models’, Nat. Hazards, vol. 106, no. 3, pp. 2461–2478, 2021. PLOS ONE Air temperature forecasting: Mutli-time scale PLOS ONE | https://doi.org/10.1371/journal.pone.0277079 November 3, 2022 29 / 31