Regional Short-term Micro-climate Air Temperature Prediction with CBPNN

Lvwen Huang; Lianliang Chen; Qin Wang; Siwen Yan; Xunbing Gao; Jiangjiang Luan

doi:10.1051/e3sconf/20185303009

All issues

Volume 53 (2018)

E3S Web Conf., 53 (2018) 03009

References

Open Access

Issue		E3S Web Conf. Volume 53, 2018 2018 3^rd 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
DOI		https://doi.org/10.1051/e3sconf/20185303009
Published online		14 September 2018

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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
K.W. Chau. Reliability and performance-based design by artificial neural network. ADV ENG SOFTW, 38(3), 145-149 (2007). [CrossRef] [Google Scholar]
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]
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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[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]