Estimation and Comparison of Probabilistic Temperatures through Using Artificial Neural Networks in Geographic Information Systems Media


Keskiner A. D. , İBRİKÇİ T. , ÇETİN M.

JOURNAL OF AGRICULTURAL SCIENCES-TARIM BILIMLERI DERGISI, cilt.17, ss.241-252, 2011 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 17 Konu: 3
  • Basım Tarihi: 2011
  • Dergi Adı: JOURNAL OF AGRICULTURAL SCIENCES-TARIM BILIMLERI DERGISI
  • Sayfa Sayısı: ss.241-252

Özet

The main objectives of this study are to develop the map of temperatures at 50% probability level through using Artificial Neural Networks method in Geographic Information System (GIS) Media and to compare GIS-based probabilistic temperatures of meteorological observation stations with the one produced by multiple regression technique in GIS media. This study was carried out in the Seyhan River Basin, covering 21,470.3 km(2) surface area. Long-term (1975-2006) annual mean temperature series of 45 meteorological observation stations of Turkish State Meteorological Service (TSMS) were utilized in this study. Meteorological stations with the record length less than 15-year were determined and record length was extended to at least 15-year through using regression analysis. Then, frequency analysis was performed on the temperature series. Kolmogorov-Smirnov goodness-of-fit test was employed to determine whether the observed temperature values of a given meteorological station came from a particular, known, and completely specified cumulative probability distribution at the 5% significance level or not. Mean temperature values with 50% probability used in M. Turc surface runoff estimation method were estimated from probability distribution models for each meteorological station. Based on the "minimum error" criterion, mean temperature map at the 50% probability level, produced by artificial neural networks, was compared to the probability temperature map produced by multiple regression technique in GIS Media. It was concluded that temperatures estimated by Adaptive Liner Neuron (ADALINE) Network Model (RMSE=0.80) were more realistic results and close in GIS media to the observed temperatures in the basin, compared to the results obtained by Multiple Regression technique (RMSE=0.82) in GIS media.