Open Access
E3S Web Conf.
Volume 53, 2018
2018 3rd International Conference on Advances in Energy and Environment Research (ICAEER 2018)
Article Number 03009
Number of page(s) 5
Section Environment Engineering, Environmental Safety and Detection
Published online 14 September 2018
  1. R.F. Chevalier, G. Hoogenboom, R.W. Mcclendon, J.A. Paz. Support vector regression with reduced training sets for air temperature prediction: a comparison with artificial neural networks. NEURAL COMPUT APPL, 20(1), 151-159 (2011). [CrossRef] [Google Scholar]
  2. M. Pramsohler, J. Hacker, G. Neuner. Freezing pattern and frost killing temperature of apple (Malus domestica) wood under controlled conditions and in nature. TREE PHYSIOL, 32(7), 819-828 (2012). [CrossRef] [PubMed] [Google Scholar]
  3. C.H. Greenberg, D.J. Levey, D.L. Loftis. Fruit Production in Mature and Recently Regenerated Forests of the Appalachians. J WILDLIFE MANAGE, 71(2): 321-335 (2011). [CrossRef] [Google Scholar]
  4. S. Briana, H. Gerrit, M.C. Ronaldw. Artificial neural networks for automated year-round temperature prediction. COMPUT ELECTRON AGR, 68(1), 52-61 (2009). [CrossRef] [Google Scholar]
  5. J. Yi, V.R. Prybutok. A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. ENVIRON POLLUT, 92(3), 349-357 (1996). [CrossRef] [Google Scholar]
  6. K. Nadig, W. Potter, G. Hoogenboom, R. Mcclendon. Comparison of individual and combined ANN models for prediction of air and dew point temperature. APPL INTELL, 39(2), 354-366 (2013). [CrossRef] [Google Scholar]
  7. D.B. Shank, G. Hoogenboom, R.W. Mcclendon. Dewpoint Temperature Prediction Using Artificial Neural Networks. J APPL METEOROL CLIM, 47(6), 1757-1769 (2006). [CrossRef] [Google Scholar]
  8. T. Kaur, S. Kumar, R. Segal. Application of artificial neural network for short term wind speed forecasting. In: 2016-Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy, January 21-23, 2016, Bengaluru, India: 1-5 (2016). [Google Scholar]
  9. H.S. Nogay, T.C. Akinci, M. Eidukeviciute. Application of artificial neural networks for short term wind speed forecasting in Mardin, Turkey. J ENERGY SOUTH AFR, 23(4), 2-7 (2012). [Google Scholar]
  10. X. Feng, Q. Li, Y. Zhu, J. Hou, L. Jin, J. Wang. Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. ATMOS ENVIRON, 107:118-128 (2015). [CrossRef] [Google Scholar]
  11. A. Castaeda-Miranda, V.M. Castao. Smart frost control in greenhouses by neural networks models. COMPUT ELECTRON AGR, 137:102-114 (2017). [CrossRef] [Google Scholar]
  12. T. Wang, K. Yang, Y. Guo. Application of Artificial Neural Networks to Forecasting Ice Conditions of the Yellow River in the Inner Mongolia Reach. J HYDROL ENG, 13(9), 811-816 (2008). [CrossRef] [Google Scholar]
  13. S. Wiesner, A. Eschenbach, F. Ament. Urban air temperature anomalies and their relation to soil moisture observed in the city of Hamburg. METEOROL Z, 23(2): 143-157(2014) [CrossRef] [Google Scholar]
  14. K.W. Chau. Reliability and performance-based design by artificial neural network. ADV ENG SOFTW, 38(3), 145-149 (2007). [CrossRef] [Google Scholar]
  15. A.J. Adeloye, A.D. Munari. Artificial neural network based generalized storage-yield-reliability models using the Levenberg-Marquardt algorithm. J HYDROL, 326(1-4): 215-230 (2006). [CrossRef] [Google Scholar]
  16. C. Igel, M. Hüsken. Empirical evaluation of the improved Rprop learning algorithms. Neurocomputing, 50(1), 105-123 (2003). [CrossRef] [Google Scholar]

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