The use of artifcial neural networks for forecasting the monthly mean soil temperatures in Adana, Turkey

Bu çalışmanın amacı, önceki aya ait bazı aylık ortalamameteorolojik değişkenleri kullanarak şu anki ayın ortalama toprak sıcaklığını tahmin etmek için bir yapay sinir ağı (YSA) modeli geliştirmektir. Bunun için, Adana meteoroloji istasyonunda 2000 ve 2007 yılları arasında ölçülen toprak sıcaklığı ve diğer meteorolojik veriler kullanıldı. Toprak sıcaklıkları Türkiye Meteoroloji İşleri Genel Müdürlüğü (DMİ) tarafından yer seviyesinden 5, 10, 20, 50 ve 100 cm derinliklerde ölçüldü. Üç katmanlı ileri beslemeli bir yapay sinir ağı yapısı oluşturuldu ve YSA’nın öğrenmesi için geri yayılım algoritması kullanıldı. Giriş değişkenleri değiştirilerek farklı modeller oluşturuldu ve ağın en iyi giriş yapısı incelendi. En iyi tahmin modelini ortaya çıkarmak için öğrenme ve test işlemlerindeki YSA modellerinin performansı ölçülen toprak sıcaklığı değerleri ile karşılaştırıldı. Elde edilen sonuçlara göre, toprak sıcaklığının tahmin edilmesi için YSA yaklaşımının çok uygun bir model olduğu görüldü.

Türkiye’nin Adana ilindeki aylık ortalama toprak sıcaklıklarının tahmini için yapay sinir ağlarının kullanımı

The objective of this paper was to develop an articial neural network (ANN)model in order to predictmonthly mean soil temperature for the presentmonth by using various previousmonthlymeanmeteorological variables. For this purpose, the measured soil temperature and other meteorological data between the years of 2000 and 2007 at Adana meteorological station were used.The soil temperatures were measured at depths of 5, 10, 20, 50, and 100 cm below the ground level by the Turkish State Meteorological Service (TSMS). A 3-layer feed-forward articial neural network structure was constructed and a back-propagation algorithm was used for the training of ANNs.The models consisting of the combination of the input variables were constructed and the best t input structure was investigated. The performances of ANN models in training and testing procedures were compared with the measured soil temperature values to identify the best t forecastingmodel.The results show that the ANN approach is a reliablemodel for prediction of monthly mean soil temperature.

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