Makine Öğrenimi Kullanarak Aylık Akarsu Akışı Tahmini

Fatih TOSUNOĞLU, Sinan HANAY, Emre ÇİNTAŞ, Barış ÖZYER
2020 Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi  
Streamflow forecasting holds a vital role in planning, design, and management of basin water resources. Accurate streamflow forecast provides a more efficient design of water resources systems technically and economically. In this study, various machine learning algorithms were evaluated to model monthly streamflow data in the Coruh river basin, Turkey. The dataset contains the mean monthly streamflow between 1963 and 2011. For the machine learning model, Support Vector Machines (SVM), Adaptive
more » ... nes (SVM), Adaptive Boosting (AdaBoost), K-Nearest Neighbours (KNN) and Random Forest algorithms were considered and compared. Based on the test scores of the considered models with the hyperparameters, Random Forest based model outperforms all other models.
doi:10.18185/erzifbed.780477 fatcat:cxfjobnorzdxdois72mjims5m4