Solar radiation and precipitable water modeling for Turkey using artificial neural networks

Senkal O.

METEOROLOGY AND ATMOSPHERIC PHYSICS, vol.127, no.4, pp.481-488, 2015 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 127 Issue: 4
  • Publication Date: 2015
  • Doi Number: 10.1007/s00703-015-0372-6
  • Page Numbers: pp.481-488


Artificial neural network (ANN) method was applied for modeling and prediction of mean precipitable water and solar radiation in a given location and given date (month), given altitude, temperature, pressure and humidity in Turkey (26-45A(0)E and 36-42A(0)N) during the period of 2000-2002. Resilient Propagation (RP) learning algorithms and logistic sigmoid transfer function were used in the network. To train the network, meteorological measurements taken by the Turkish State Meteorological Service (TSMS) and Wyoming University for the period from 2000 to 2002 from five stations distributed in Turkey were used as training data. Data from years (2000 and 2001) were used for training, while the year 2002 was used for testing and validating the model. The RP algorithm were first used for determination of the precipitable water and subsequently, computation of the solar radiation, in these stations Root Mean Square Error (RMSE) between the estimated and measured values for monthly mean daily sum for precipitable water and solar radiation values have been found as 0.0062 gr/cm(2) and 0.0603 MJ/m(2) (training cities), 0.5652 gr/cm(2) and 3.2810 MJ/m(2) (testing cities), respectively.