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

Ahmet İrvem; Mustafa Özbuldu

Makale detayları

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

Yazarlar:

Ahmet İrvem | Mustafa Özbuldu

Yayıncı:

Cukurova University

Ana Başlıklar:

Biyosistem

Dergi:

Çukurova Tarım ve Gıda Bilimleri Dergisi Vol.39 Issue 2, pp.401-416

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001337433

pISSN:

2636-7874

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

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

Yazarlar:

Ahmet İrvem | Mustafa Özbuldu

Yayıncı:

Cukurova University

Ana Başlıklar:

Biyosistem

Dergi:

Çukurova Tarım ve Gıda Bilimleri Dergisi Vol.39 Issue 2, pp.401-416

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001337433

pISSN:

2636-7874

Artificial neural networks

support vector regression

MATLAB

streamflow estimation

MERRA-2Yapay sinir ağları

destek vektör regresyonu

MATLAB

akış tahmini

MERRA-2

Sayı

Makale detayları

Yazarlar: Ahmet İrvem | Mustafa Özbuldu

Yayıncı: Cukurova University

Ana Başlıklar: Biyosistem

Dergi: Çukurova Tarım ve Gıda Bilimleri Dergisi Vol.39 Issue 2, pp.401-416

Belge Tipi: Makale

Yayın Tarihi: 12/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001337433

pISSN: 2636-7874

Sayı

Yazarlar:

Ahmet İrvem | Mustafa Özbuldu

Yayıncı:

Cukurova University

Ana Başlıklar:

Biyosistem

Dergi:

Çukurova Tarım ve Gıda Bilimleri Dergisi Vol.39 Issue 2, pp.401-416

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001337433

pISSN:

2636-7874