FIRAT HAVZASI KARASU NEHRİNDEKİ AYLIK AKIMLARIN YAPAY SİNİR AĞLARI YAKLAŞIMINI İLE TAHMİNİ

Abstract

Bu çalışma çeşitli meteorolojik parametreler kullanarak yapay sinir ağları (ANN) ile akım değerlerinin tahmin edilmesini amaçlanmaktadır. ANN modelinin geliştirilmesinde yağış, hava sıcaklıkları ve potansiyel evapotranspirasyon değerlerinin çeşitli kombinasyonları girdi olarak kullanılmış ve akım değerleri elde edilmiştir. Modelin tasarlanmasında çeşitli eğitim algoritmaları, ağ mimarisi, girdi kombinasyonları ve isterasyon sayıları denenerek en uygun model sınanmıştır. Korelasyon katsayısı (R), belirlilik katsayısı (R2), mutlak hata (AE) ve göreceli mutlak hata (ARE) katsayıları karşılaştırılarak en uygun model seçilmiştir. Analiz sonuçlarına göre 2000 iterasyon, 4-4-1 modelinin mimarisi ve Quasi-Newton algoritması kullanılarak optimal model elde edilmiştir. ANN modellerinin yağış-akış ilişkisini modellemede ve güvenilir tahminler üretmede başarılı olduğu tespit edilmiştir. Ayrıca Thornthwaite yöntemi ile elde edilen potansiyel evapotranspirasyon değerlerinin modele dâhil edilmesinin modelin başarısını arttırdığı ortaya konmuştur.

Keywords

• Yapay Sinir Ağı
• Yağış-Akış İlişkisi
• Hava sıcaklığı
• Potansiyel Evapotranspirasyon
• Karasu Aşağıkağdariç

MONTHLY STREAM FLOWS ESTIMATION IN THE KARASU RIVER OF EUPHRATES BASIN WITH ARTIFICIAL NEURAL NETWORKS APPROACH

Abstract

This study aims to estimate streamflow values with artificial neural networks (ANN) using various meteorological parameters. In developing the ANN model, various combinations of precipitation, air temperatures, and potential evapotranspiration values were used as inputs, and streamflow values were obtained. Meteorological data is divided into 70% train, 15% test, and 15% validation. In the model's design, various training algorithms, network architecture, input combinations, and the number of iterations were tried, and the most suitable model was tested. Correlation coefficient (R), coefficient of determination (R2), absolute error (AE), and absolute relative error (ARE) coefficients were compared, and the most suitable model was selected. According to the analysis results, the optimal model was obtained using 2000 iterations, the architecture of the 4-4-1 model, and the Quasi-Newton algorithm. It was determined that the ANNs successfully modeled the rainfall-runoff relationship and produced reliable estimates. In addition, it was revealed that the inclusion of potential evapotranspiration values obtained by the Thornthwaite method into the model increases the model's success.

Keywords

• Artificial Neural Network
• Rainfall-Runoff Relationship
• Air Temperature
• Potential Evapotranspiration
• Karasu Aşağıkağdariç

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