The Estimation of Monthly Mean Soil Temperature at Different Depths in Sivas Province, Turkey by Artificial Neural Networks

Gurlek C.

COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS, cilt.54, sa.3, ss.408-430, 1 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 54 Sayı: 3
  • Basım Tarihi: 1
  • Doi Numarası: 10.1080/00103624.2022.2116032
  • Dergi Adı: COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Agricultural & Environmental Science Database, Aqualine, BIOSIS, CAB Abstracts, Chemical Abstracts Core, Chimica, Environment Index, Geobase, Pollution Abstracts, Veterinary Science Database
  • Sayfa Sayıları: ss.408-430
  • Anahtar Kelimeler: Air temperature, artificial neural networks, Sivas province, soil temperature
  • Sivas Cumhuriyet Üniversitesi Adresli: Hayır

Özet

In this study, soil temperature of Sivas province was estimated by the artificial neural networks (ANNs) method using data obtained from five different meteorological measurement stations situated in provincial borders. Nineteen years of (2000-2018) monthly mean air temperature data obtained from five different soil depths (5, 10, 20, 50 and 100 cm) was used for ANN analysis. Predicted and measured soil temperatures were strongly correlated with determination coefficient (R-2) values ranging between 0.9767 and 0.9941. Mean Absolute Error (MAE) ranged from 0.532 degrees C to 1.381 degrees C, while Mean Absolute Percentage Error (MAPE) ranged from 5.692% to 16.263% and Root Mean Squared Error (RMSE) ranged between 0.694 degrees C and 1.666 degrees C. It was found that the predicted values are in good agreement with the measured data. However, there was a tendency to underestimate the soil temperature.