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Soil Temperature Prediction Using Time-Delay Neural Networks

  • E. Mazou
  • N. Alvertos
  • I. X. Tsiros
Conference paper
Part of the Springer Atmospheric Sciences book series (SPRINGERATMO)

Abstract

Neural networks are widely used for time series prediction in the recent years. In particular, dynamic neural networks with embedded time delays are the most appropriate models for the simulation of nonlinear processes since they make use the effect of past input values. The purpose of this study is to predict soil temperature in various depths, by using dynamic neural networks. The dynamic networks used are recurrent neural networks with feedback loop that includes time-delay elements. The data used for the neural network’s training, validation and testing were hourly values obtained from the weather station at the Agricultural University of Athens, for the period 2002–2005. Error statistics of the results showed a good fitting of the models.

Keywords

Soil Temperature Mean Square Error Recurrent Neural Network Recurrent Network Neural Network Toolbox 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. AbdAlKader SA, AL-Allaf ONA (2011) Backpropagation neural network algorithm for forecasting soil temperatures considering many aspects: a comparison of different approaches. In: Proceedings of the 5th international conference on information technology, Amman, 11–13 May 2011Google Scholar
  2. Beale MH, Hagan MT, Demuth HB (2011) Neural network toolbox getting started guide R2011b. http://mathworks.com/help/pdf_doc/nnet/nnet_gs.pdf. Accessed on 25 November 2011
  3. Connor J, Martin D, Atlas L (1994) Recurrent neural networks and robust time series prediction. IEEE Trans Neural Networks 5:240–254. doi: 1045-9227/94S04.00 CrossRefGoogle Scholar
  4. Diamantopoulou MJ, Georgiou PE, Papamichail DM (2010) Performance evaluation of artificial neural networks in estimating reference evapotranspiration with minimal meteorological data. Global NEST J 13:18–27Google Scholar
  5. Tasadduq I, Rehman S, Budshait K (2002) Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia. Renew Energ 25:545–554. doi: 10.1016/S0960-1481(01)00082-9 CrossRefGoogle Scholar
  6. Veronez MR, Thum AB, Luz AS, da Silva DR (2006) Artificial neural networks applied in the determination of soil surface temperatures – SST. In: Proceedings of 7th international symposium on spatial accuracy assessment in nature resources and environmental sciences, LisbonGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Agricultural University of AthensAthensGreece

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