Data division effect on machine learning performance for prediction of streamflow

Okan Mert Katipoğlu

Makale detayları

Data division effect on machine learning performance for prediction of streamflow

Yazarlar:

Okan Mert Katipoğlu

Yayıncı:

Dicle University

Dergi:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.13 Issue 4, pp.653-660

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001281805

pISSN:

1309-8640

Anahtar kelimeler:

Özet:

 Accurate estimation of streamflow has an important role in water resources management, disaster preparedness and early warning, reservoir operation, and sizing of water structures. In this study, Extreme gradient boosting XGBoost and K-Nearest Neighbours KNN algorithms are used for the estimation of streamflow. In order to reveal the appropriate model, the raw model and models with optimized parameters were evaluated while the models were being built. In the setup of the models, various training test rates were also tried, and it was investigated which data division showed more effective results. For this purpose, the data were divided into ratios such as 60-40, 70-30, 80-20, and 90-10, respectively, and the model results were compared. Various statistical indicators such as Root Mean Square Error RMSE, Mean Absolute Error MAE, and Coefficient of Determination R2 were used when comparing the models. As a result of the analysis, it was determined that the most suitable model for monthly streamflow estimation was obtained by using the optimized Xgboost algorithm and 60-40 data division. The obtained outputs constitute a vital resource for decision-makers regarding water resources planning and flood and drought management.

 Accurate estimation of streamflow has an important role in water resources management, disaster preparedness and early warning, reservoir operation, and sizing of water structures. In this study, Extreme gradient boosting XGBoost and K-Nearest Neighbours KNN algorithms are used for the estimation of streamflow. In order to reveal the appropriate model, the raw model and models with optimized parameters were evaluated while the models were being built. In the setup of the models, various training test rates were also tried, and it was investigated which data division showed more effective results. For this purpose, the data were divided into ratios such as 60-40, 70-30, 80-20, and 90-10, respectively, and the model results were compared. Various statistical indicators such as Root Mean Square Error RMSE, Mean Absolute Error MAE, and Coefficient of Determination R2 were used when comparing the models. As a result of the analysis, it was determined that the most suitable model for monthly streamflow estimation was obtained by using the optimized Xgboost algorithm and 60-40 data division. The obtained outputs constitute a vital resource for decision-makers regarding water resources planning and flood and drought management.

 Accurate estimation of streamflow has an important role in water resources management, disaster preparedness and early warning, reservoir operation, and sizing of water structures. In this study, Extreme gradient boosting XGBoost and K-Nearest Neighbours KNN algorithms are used for the estimation of streamflow. In order to reveal the appropriate model, the raw model and models with optimized parameters were evaluated while the models were being built. In the setup of the models, various training test rates were also tried, and it was investigated which data division showed more effective results. For this purpose, the data were divided into ratios such as 60-40, 70-30, 80-20, and 90-10, respectively, and the model results were compared. Various statistical indicators such as Root Mean Square Error RMSE, Mean Absolute Error MAE, and Coefficient of Determination R2 were used when comparing the models. As a result of the analysis, it was determined that the most suitable model for monthly streamflow estimation was obtained by using the optimized Xgboost algorithm and 60-40 data division. The obtained outputs constitute a vital resource for decision-makers regarding water resources planning and flood and drought management.

 Accurate estimation of stream flow has an important role in water resources management, disaster preparedness and early warning, reservoir operation, and sizing of water structures. In this study, Extreme gradient boosting XGBoost and K-Nearest Neighbours KNN algorithms are used for modeling river flows. In order to reveal the appropriate model, the raw model and models with optimized parameters were evaluated while the models were being built. In the setup of the models, various training test rates were also tried, and it was investigated which data division showed more effective results. For this purpose, the data were divided into ratios such as 60-40, 70-30, 80-20, and 90-10, respectively, and the model results were compared. Various statistical indicators such as root mean square error RMSE, mean absolute error MAE, and coefficient of determination R2 were used when comparing the models. As a result of the analysis, it was determined that the most suitable model for monthly flow estimation was obtained by using the optimized Xgboost algorithm and 60-40 data division. The obtained outputs constitute a vital resource for decision-makers regarding water resources planning and flood and drought management.

Makale detayları

Data division effect on machine learning performance for prediction of streamflow

Yazarlar:

Okan Mert Katipoğlu

Yayıncı:

Dicle University

Dergi:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.13 Issue 4, pp.653-660

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001281805

pISSN:

1309-8640

Stream flows

XGBoost

K-Nearest Neighbours

Data division

Euphrates basin

Stream flows

XGBoost

K-Nearest Neighbours

Data division

Euphrates basin

Sayı

Makale detayları

Yazarlar: Okan Mert Katipoğlu

Yayıncı: Dicle University

Dergi: Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.13 Issue 4, pp.653-660

Belge Tipi: Makale

Yayın Tarihi: 3/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001281805

pISSN: 1309-8640

Sayı

Yazarlar:

Okan Mert Katipoğlu

Yayıncı:

Dicle University

Dergi:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.13 Issue 4, pp.653-660

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001281805

pISSN:

1309-8640