Estimation of missing temperature data by Artificial Neural Network (ANN)

Abstract

Ensuring more reliable and quality meteorological and climatological studies by providing data continuity and widening the data range. For this reason, missing values in meteorological data such as temperature, precipitation, evaporation must be completed. In this study, an artificial neural network (ANN) model was used to complete missing temperature data in the Horasan meteorology station. To establish the ANN model, monthly average temperature values of neighboring stations having similar climatic characteristics and altitude with Horasan were used as input. The monthly average temperature values of the Horasan station were used as output. Approximately 70% of the data was used for training, about 15% for testing, and about 15% for verification in the ANN model. Various statistical parameters were compared to determine the best network architecture and best model. As a result, the model's high determination coefficient (R2 = 0.99) and low mean absolute error (MAE = 0.61) showed that the ANN model can be used effectively in estimating missing temperature data.

Keywords

• Missing data
• Temperature
• Artificial Neural Network (ANN)
• Network training
• Network testing and validation

Eksik sıcaklık verilerinin Yapay Sinir Ağları (YSA) ile tahmin edilmesi

Abstract

Veri sürekliliğinin sağlanması ve aralığın genişletilmesi ile meteorolojik ve klimatolojik çalışmaların daha güvenilir ve kaliteli olmasını sağlamaktadır. Bu nedenle sıcaklık, yağış, buharlaşma gibi meteorolojik verilerde eksik olan değerlerin tamamlanması gerekmektedir. Bu çalışmada, Horasan meteoroloji istasyonundaki eksik sıcaklık verilerini tamamlamak için Yapay sinir ağı (YSA) modeli kullanılmıştır. YSA modelinin kurulması için Horasan ile benzer iklim özelliklerine ve rakıma sahip komşu istasyonların aylık ortalama sıcaklık değerleri girdi olarak kullanılmıştır. Horasan istasyonunun aylık ortalama sıcaklık değerleri ise çıkış olarak kullanılmıştır. YSA modelinde verilerin yaklaşık% 70'i eğitim için, yaklaşık% 15'i test için ve yaklaşık% 15'i doğrulama için kullanılmıştır. En iyi ağ mimarisini ve en iyi modeli belirlemek için çeşitli istatistiksel parametreler karşılaştırılmıştır. Sonuç olarak, modelin yüksek belirlilik katsayısı (R2 = 0.99) ve düşük ortalama mutlak hataya (OMH = 0.61) sahip olması YSA modelinin eksik sıcaklık verilerini tahmin etmede etkin bir şekilde kullanılabileceğini göstermiştir.

Keywords

• Eksik veri
• Sıcaklık
• Yapay Sinir Ağları (YSA)
• Ağın eğitimi
• Ağın test edilmesi ve doğrulanması

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