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.
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Mazou, E., Alvertos, N., Tsiros, I.X. (2013). Soil Temperature Prediction Using Time-Delay Neural Networks. In: Helmis, C., Nastos, P. (eds) Advances in Meteorology, Climatology and Atmospheric Physics. Springer Atmospheric Sciences. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29172-2_87
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DOI: https://doi.org/10.1007/978-3-642-29172-2_87
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