Derin sinir ağları modeli ile standardize yağış indeksi tahmini

Kuraklık, yaşamı doğrudan etkileyen ve çok çeşitli olumsuz etkileri olan doğal bir afettir. Kuraklığı tahmin etmek üzere farklı kuraklık indeksleri kullanılmaktadır. Bu indekslerden en yaygın olarak kullanılanlardan biri de Standardize Yağış İndeksidir (SYİ). Gerçekleştirilen çalışmada Türkiye’ye ait Rize, Konya ve Şanlıurfa illerinin 3, 6, 9 ve 12 aylık SYİ verileri 1-3 ileri zamanlı olarak tahmin edilmiştir. Tahmin çalışmasını gerçekleştirmek üzere Uzun Kısa Süreli Bellek Ağları (Long Short Term Memory Networks, LSTM) ve Çift Yönlü Uzun Kısa Süreli Bellek Ağlarından (Bidirectional Long Short Term Memory Networks, biLSTM) oluşan Derin Sinir Ağları modelleri geliştirilmiştir. Tahmin performansını değerlendirmek üzere Ortalama Mutlak Hata (Mean Absolute Error, MAE), Ortalama Karesel Hata (Mean Squared Error, MSE), Korelasyon katsayısı (Correlation Coefficient, R) ve Belirlilik katsayısı (Determination Coefficient, R2) parametreleri kullanılmıştır. Elde edilen sonuçlar tahmin parametreleri ve saçılma grafikleri ile değerlendirildiğinde biLSTM içeren derin sinir ağları modelinin performansının oldukça iyi olduğu ve 3 ileri zamanlı tahminde bile yüksek korelasyona sahip sonuçlar elde edilebileceğini göstermiştir.

Anahtar Kelimeler:

Kuraklık, Standardize yağış indeksi, Derin sinir ağları, LSTM, biLSTM

Standard precipitation index estimation with deep neural network model

Drought is a natural disaster that directly affects life and has a wide variety of negative effects. Different drought indices are used to predict drought. One of the most widely used of these indices is the Standardized Precipitation Index (SPI). In this study, the 3, 6, 9 and 12-month SPI data of Rize, Konya and Şanlıurfa provinces of Turkey were estimated 1-3 forward time. Deep Neural Networks models consisting of Long Short Term Memory Networks (LSTM) and Bidirectional Long Short Term Memory Networks (biLSTM) have been developed to perform the prediction study. The Mean Absolute Error (MAE), Mean Squared Error (MSE), Correlation Coefficient, R and Determination Coefficient (R2) parameters were used to evaluate the forecasting performance. When the results obtained are evaluated with the performance parameters and scatter plots, it has been shown that the performance of the deep neural network model with biLSTM is quite good and that high correlation results can be obtained even in 3 forward-time predictions.

Keywords:

Drought, Standard precipitation index, Deep neural networks, LSTM, biLSTM,

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