Ceyhan Nehir Havzası için Yapay Sinir Ağları, Destek Vektör Regresyonu ve Çoklu Doğrusal Regresyon Modelleri Kullanılarak Günlük Akış Değerlerinin Tahmini

Year 2024, Volume: 39 Issue: 2, 401 - 416, 30.12.2024

Ahmet İrvem , Mustafa Özbuldu

Abstract

Akış verileri, havzalardaki su kaynaklarının etkin planlanması ve yönetilebilmesi için oldukça önemlidir. Bu çalışmada, Ceyhan Nehri Havzasında bulunan üç farklı akarsuyun günlük akışını tahmin etmek için Yapay Sinir Ağları, Destek Vektör Regresyonu ve Çoklu Doğrusal Regresyon modelleri oluşturulmuştur. Modellerde tahmin edici değişkenler olarak MERRA-2 re-analiz veri setinden elde edilen günlük yağış ve sıcaklık verileri kullanılmıştır. Modellerin tahmin performansları farklı istatistiksel performans ölçütleri ile değerlendirilmiştir. Değerlendirme sonuçlarına göre SVR modelinin günlük akarsu akışını diğer modellere göre daha başarılı bir şekilde tahmin ettiği belirlenmiştir. Ayrıca makine öğrenmesi algoritmaları kullanılarak oluşturulan modellerin performansının doğrusal regresyon modeline göre daha üstün olduğu tespit edilmiştir.

Keywords

Yapay sinir ağları, destek vektör regresyonu, MATLAB, akış tahmini, MERRA-2

Estimating Daily Streamflow Values Using Artificial Neural Networks, Support Vector Regression and Multiple Linear Regression Models for Ceyhan River Basin

Abstract

Streamflow data are very important for effective planning and management of water resources in basins. In this study, Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Multiple Linear Regression (MLR) models were developed to estimate the daily streamflow of three different rivers in the Ceyhan River Basin. Daily precipitation and temperature data obtained from The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) re-analysis data were used as predictor variables in the models. The estimation performances of the models were evaluated with different statistical performance measures. According to the evaluation results, the SVR model demonstrated the best performance in daily streamflow estimation for the Ceyhan River, achieving R² = 0.95 and RMSE = 28.20 m³ s-1. Additionally, for Söğütlü Creek, the results were R² = 0.82 and RMSE = 6.57 m³ s-1, while for Keşiş Creek, R² = 0.93 and RMSE = 1.45 m³ s-1 were obtained. The findings indicate that the SVR model predicts daily streamflow more successfully than the other models. Furthermore, it was found that the performance of the models developed using machine learning algorithms was superior to that of the linear regression model.

Keywords

Artificial neural networks, support vector regression, MATLAB, streamflow estimation, MERRA-2

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