Akademik Veri Yönetim Sistemi
Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, cilt.13, sa.4, ss.653-660, 2022 (Hakemli Dergi)
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.