Application of Soft Computing Techniques in River Flow Modeling

Sefa Nur Yesilyurt; Hüseyin Yildirim Dalkılıç; Pijush Samuı

Makale detayları

Application of Soft Computing Techniques in River Flow Modeling

Yazarlar:

Sefa Nur Yesilyurt | Hüseyin Yildirim Dalkılıç | Pijush Samuı

Yayıncı:

Sakarya University

Ana Başlıklar:

Civil Engineering

Dergi:

Sakarya University Journal of Science Vol.27 Issue 3, pp.634-642

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001292196

pISSN:

2147-835X

Anahtar kelimeler:

Özet:

 Modeling of data is critical in the analysis and evaluation of hydrological behavior. River flow data is one of the most important data in explaining hydrology. Management of water resources; It takes place in the literature as an area that needs to be investigated in order to provide early warning for undesirable situations such as floods and drought. For this reason, it is of important to develop different techniques for the estimation and modeling of river flow or to make comparisons between techniques. In this study, the flow data of fourteen stations located in the Euphrates-Tigris basin between 1981 and 2010 were used. Adaptive Network Based Fuzzy Inference Systems ANFIS, Support Vector Machine SVM techniques that are frequently used in the literature, and newly introduced Gaussian Process Regression GPR, Extreme Learning Machine ELM and Emotional Neural Network ENN artificial intelligence techniques are compared. In addition, considering all performance indices, it was determined which technique gave better results with rank analysis. Although all models worked well, it was seen that the methods were ranked as ELM, GPR, ENN, SVM and ANFIS starting from the best. This has shown that ELM, GPR and ENN methods, which have been used recently in flow modeling, give better results than traditional methods with complex structures. In addition, flow values were used in the whole study and these values were examined in 3 different combinations. It was seen that the model structure that gave the best performance was the model structure that used the flow data from one, two and three days ago as an estimator. The results were analyzed with a Taylor diagram and time series graphs.

 Modeling of data is critical in the analysis and evaluation of hydrological behavior. River flow data is one of the most important data in explaining hydrology. Management of water resources; It takes place in the literature as an area that needs to be investigated in order to provide early warning for undesirable situations such as floods and drought. For this reason, it is of important to develop different techniques for the estimation and modeling of river flow or to make comparisons between techniques. In this study, the flow data of fourteen stations located in the Euphrates-Tigris basin between 1981 and 2010 were used. Adaptive Network Based Fuzzy Inference Systems ANFIS, Support Vector Machine SVM techniques that are frequently used in the literature, and newly introduced Gaussian Process Regression GPR, Extreme Learning Machine ELM and Emotional Neural Network ENN artificial intelligence techniques are compared. In addition, considering all performance indices, it was determined which technique gave better results with rank analysis. Although all models worked well, it was seen that the methods were ranked as ELM, GPR, ENN, SVM and ANFIS starting from the best. This has shown that ELM, GPR and ENN methods, which have been used recently in flow modeling, give better results than traditional methods with complex structures. In addition, flow values were used in the whole study and these values were examined in 3 different combinations. It was seen that the model structure that gave the best performance was the model structure that used the flow data from one, two and three days ago as an estimator. The results were analyzed with a Taylor diagram and time series graphs.

 Modeling of data is critical in the analysis and evaluation of hydrological behavior. River flow data is one of the most important data in explaining hydrology. Management of water resources; It takes place in the literature as an area that needs to be investigated in order to provide early warning for undesirable situations such as floods and drought. For this reason, it is of important to develop different techniques for the estimation and modeling of river flow or to make comparisons between techniques. In this study, the flow data of fourteen stations located in the Euphrates-Tigris basin between 1981 and 2010 were used. Adaptive Network Based Fuzzy Inference Systems ANFIS, Support Vector Machine SVM techniques that are frequently used in the literature, and newly introduced Gaussian Process Regression GPR, Extreme Learning Machine ELM and Emotional Neural Network ENN artificial intelligence techniques are compared. In addition, considering all performance indices, it was determined which technique gave better results with rank analysis. Although all models worked well, it was seen that the methods were ranked as ELM, GPR, ENN, SVM and ANFIS starting from the best. This has shown that ELM, GPR and ENN methods, which have been used recently in flow modeling, give better results than traditional methods with complex structures. In addition, flow values were used in the whole study and these values were examined in 3 different combinations. It was seen that the model structure that gave the best performance was the model structure that used the flow data from one, two and three days ago as an estimator. The results were analyzed with a Taylor diagram and time series graphs.

 Modeling of data is critical in the analysis and evaluation of hydrological behavior. River flow data is one of the most important data in explaining hydrology. Management of water resources; It takes place in the literature as an area that needs to be investigated in order to provide early warning for undesirable situations such as floods and drought. For this reason, it is of important to develop different techniques for the estimation and modeling of river flow or to make comparisons between techniques. In this study, the flow data of fourteen stations located in the Euphrates-Tigris basin between 1981 and 2010 were used. Adaptive Network Based Fuzzy Inference Systems ANFIS, Support Vector Machine SVM techniques that are frequently used in the literature, and newly introduced Gaussian Process Regression GPR, Extreme Learning Machine ELM and Emotional Neural Network ENN artificial intelligence techniques are compared. In addition, considering all performance indices, it was determined which technique gave better results with rank analysis. Although all models worked well, it was seen that the methods were ranked as ELM, GPR, ENN, SVM and ANFIS starting from the best. This has shown that ELM, GPR and ENN methods, which have been used recently in flow modeling, give better results than traditional methods with complex structures. In addition, flow values were used in the whole study and these values were examined in 3 different combinations. It was seen that the model structure that gave the best performance was the model structure that used the flow data from one, two and three days ago as an estimator. The results were analyzed with a Taylor diagram and time series graphs.

Makale detayları

Application of Soft Computing Techniques in River Flow Modeling

Yazarlar: Sefa Nur Yesilyurt | Hüseyin Yildirim Dalkılıç | Pijush Samuı

Yayıncı: Sakarya University

Ana Başlıklar: Civil Engineering

Dergi: Sakarya University Journal of Science Vol.27 Issue 3, pp.634-642

Belge Tipi: Makale

Yayın Tarihi: 6/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001292196

pISSN: 2147-835X

ANFIS

ELM

ENN

GPR

SVM

Sayı

Yazarlar:

Sefa Nur Yesilyurt | Hüseyin Yildirim Dalkılıç | Pijush Samuı

Yayıncı:

Sakarya University

Ana Başlıklar:

Civil Engineering

Dergi:

Sakarya University Journal of Science Vol.27 Issue 3, pp.634-642

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001292196

pISSN:

2147-835X