Monthly Streamflow Prediction Using ANN, KNN and ANFIS models: Example of Gediz River Basin

Naz'm Nazımı; Kemal Saplıoğlu

Makale detayları

Monthly Streamflow Prediction Using ANN, KNN and ANFIS models: Example of Gediz River Basin

Yazarlar:

Naz'm Nazımı | Kemal Saplıoğlu

Yayıncı:

Isparta University of Applied Sciences

Ana Başlıklar:

Mühendislik

Dergi:

Teknik Bilimler Dergisi Vol.13 Issue 2, pp.42-49

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001293238

pISSN:

2146-2119

Anahtar kelimeler:

Özet:

 Stream flow forecasting is very important in many aspects such as water supply, irrigation, building water infrastructures, and taking precautions against floods. The ability to forecast future streamflow helps us anticipate and plan for upcoming flooding, decreasing property destruction, preventing deaths and managing water in the best way possible. Different hydrological models have been developed for predicting streamflow and they have different characteristics, driven by the research area and available data. İn this study, three types of Artificial Intelligence models; K-Nearest Neighbor KNN, Artificial Neural Network ANN and Adaptive Neuro Fuzzy Inference System ANFIS have been used to study the Gediz River Basin which is located in the Aegean region of western Turkey. The results varied due to the complication of the data and different parts of the study area as well as the structure of the models, over all, looking at Regression coefficient R2, Root Mean Square Error RMSE and Wilcoxon WT values, ANFIS is more accurate compared to ANN and KNN models. Conversely, according to Taylor diagram, KNN is more accurate compared to ANN and ANFIS.

 Stream flow forecasting is very important in many aspects such as water supply, irrigation, building water infrastructures, and taking precautions against floods. The ability to forecast future streamflow helps us anticipate and plan for upcoming flooding, decreasing property destruction, preventing deaths and managing water in the best way possible. Different hydrological models have been developed for predicting streamflow and they have different characteristics, driven by the research area and available data. İn this study, three types of Artificial Intelligence models; K-Nearest Neighbor KNN, Artificial Neural Network ANN and Adaptive Neuro Fuzzy Inference System ANFIS have been used to study the Gediz River Basin which is located in the Aegean region of western Turkey. The results varied due to the complication of the data and different parts of the study area as well as the structure of the models, over all, looking at Regression coefficient R2, Root Mean Square Error RMSE and Wilcoxon WT values, ANFIS is more accurate compared to ANN and KNN models. Conversely, according to Taylor diagram, KNN is more accurate compared to ANN and ANFIS.

 Stream flow forecasting is very important in many aspects such as water supply, irrigation, building water infrastructures, and taking precautions against floods. The ability to forecast future streamflow helps us anticipate and plan for upcoming flooding, decreasing property destruction, preventing deaths and managing water in the best way possible. Different hydrological models have been developed for predicting streamflow and they have different characteristics, driven by the research area and available data. İn this study, three types of Artificial Intelligence models; K-Nearest Neighbor KNN, Artificial Neural Network ANN and Adaptive Neuro Fuzzy Inference System ANFIS have been used to study the Gediz River Basin which is located in the Aegean region of western Turkey. The results varied due to the complication of the data and different parts of the study area as well as the structure of the models, over all, looking at Regression coefficient R2, Root Mean Square Error RMSE and Wilcoxon WT values, ANFIS is more accurate compared to ANN and KNN models. Conversely, according to Taylor diagram, KNN is more accurate compared to ANN and ANFIS.

 Akarsu akış tahmini, su temini, sulama, su altyapılarının inşası, taşkınlara karşı önlem alınması gibi birçok konu için çok önemlidir. Gelecekteki nehir akışını tahmin etme yeteneği, yaklaşan selleri tahmin etmemize ve planlamamıza, mülk tahribatını azaltmamıza, ölümleri önlememize ve suyu mümkün olan en iyi şekilde yönetmemize yardımcı olur. Akarsu akışını tahmin etmek için farklı hidrolojik modeller geliştirilmiştir. Bu modeller, araştırma alanı ve mevcut veriler tarafından yönlendirilen farklı özelliklere sahiptirler. Bu çalışmada, K-En Yakın Komşu KNN, Yapay Sinir Ağı ANN ve Uyarlanabilir Nöro Bulanık Çıkarım Sistemi ANFIS, olarak üç farklı yapay zeka modeli kullanılmıştır. Türkiyenin batısındaki Ege bölgesinde yer alan Gediz Nehri Havzasının verileri ise eğitim ve test için kullanılmıştır. Sonuçlar, verilerin karmaşıklığı ve çalışma alanının farklı bölümleri ve ayrıca modellerin yapısı nedeniyle değişiklik göstermiştir, genel olarak, Regresyon katsayısı R², Ortalama Karesel Hata RMSE ve Wilcoxon WT değerlerine bakıldığında ANFIS, YSA ve KNN modellerine kıyasla daha doğrudur. Taylor diyagramına göre ise KNN, ANN ve ANFISe kıyasla daha doğrudur.

Makale detayları

Monthly Streamflow Prediction Using ANN, KNN and ANFIS models: Example of Gediz River Basin

Yazarlar:

Naz'm Nazımı | Kemal Saplıoğlu

Yayıncı:

Isparta University of Applied Sciences

Ana Başlıklar:

Mühendislik

Dergi:

Teknik Bilimler Dergisi Vol.13 Issue 2, pp.42-49

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001293238

pISSN:

2146-2119

Streamflow Prediction

ANFIS

ANN

KNN

Gediz River Basin

WilcoxonAkış Tahmini

ANFIS

ANN

KNN

Gediz Nehri Havzası

Wilcoxon

Sayı

Makale detayları

Yazarlar: Naz'm Nazımı | Kemal Saplıoğlu

Yayıncı: Isparta University of Applied Sciences

Ana Başlıklar: Mühendislik

Dergi: Teknik Bilimler Dergisi Vol.13 Issue 2, pp.42-49

Belge Tipi: Makale

Yayın Tarihi: 1/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001293238

pISSN: 2146-2119

Sayı

Yazarlar:

Naz'm Nazımı | Kemal Saplıoğlu

Yayıncı:

Isparta University of Applied Sciences

Ana Başlıklar:

Mühendislik

Dergi:

Teknik Bilimler Dergisi Vol.13 Issue 2, pp.42-49

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001293238

pISSN:

2146-2119