Artificial Neural Network Modeling of Relative Humidity and Air Temperature Spatial and Temporal Distributions Over Complex Terrains

  • Kostas Philippopoulos
  • Despina Deligiorgi
  • Georgios Kouroupetroglou
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 318)


In this work we present a methodological approach of applying Artificial Neural Networks (ANN) for modeling of both the air temperature (AT) and relative humidity (RH) spatial and temporal distributions over complex terrains. A number of implementation issues are discussed, along with their relative advantages and limitations. Moreover, after the introduction of a set of metrics, the accuracy of the evaluation of ANN based spatial and time series AT and RH modeling in the case of a specific region is examined, by applying a number of alternative feed forward ANN topologies. The Levenberg-Marquardt back propagation algorithm was used for the ANNs training in the temporal forecasting of AT and RH, with the optimum architecture being the one that minimizes the Mean Absolute Error on the validation set. The Radial Basis Function and the Multilayer Perceptrons non-linear Feed Forward ANNs schemes are compared for the spatial estimation of AT and RH. We found that the spatial and temporal AT and RH variability over complex terrains can be modeled efficiently by ANNs.


Artificial neural networks Relative humidity modeling Air temperature modeling Spatial interpolation Time-series forecasting 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Kostas Philippopoulos
    • 1
  • Despina Deligiorgi
    • 1
  • Georgios Kouroupetroglou
    • 2
  1. 1.Division of Environmental Physics and Meteorology, Department of PhysicsNational and Kapodistrian University of AthensAthensGreece
  2. 2.Division of Communication and Signal Processing, Department of Informatics and TelecommunicationsNational and Kapodistrian University of AthensAthensGreece

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