Data division effect on machine learning performance for prediction of streamflow

Authors : Okan Mert KATİPOĞLU

Pages : 653-660

Doi:10.24012/dumf.1158748

View : 20 | Download : 9

Publication Date : 2023-01-03

Article Type : Research Paper

Abstract :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 insert ignore into journalissuearticles values(XGBoost); and K-Nearest Neighbours insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(RMSE);, Mean Absolute Error insert ignore into journalissuearticles values(MAE);, and Coefficient of Determination insert ignore into journalissuearticles values(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.
Keywords : Stream flows, XGBoost, K Nearest Neighbours, Data division, Euphrates basin