Monthly Streamflow Prediction Using ANN, KNN and ANFIS models: Example of Gediz River Basin

Authors : Naz\`m NAZIMI, Kemal SAPLIOĞLU

Pages : 42-49

Doi:10.35354/tbed.1298296

View : 37 | Download : 54

Publication Date : 2023-08-01

Article Type : Research Paper

Abstract :Stream flow forecasting is very important in many aspects such as water supply, irrigation, building water infrastructures, and taking precautions against floods. The ability to forecast future streamflow helps us anticipate and plan for upcoming flooding, decreasing property destruction, preventing deaths and managing water in the best way possible. Different hydrological models have been developed for predicting streamflow and they have different characteristics, driven by the research area and available data. İn this study, three types of Artificial Intelligence models; K-Nearest Neighbor insert ignore into journalissuearticles values(KNN);, Artificial Neural Network insert ignore into journalissuearticles values(ANN); and Adaptive Neuro Fuzzy Inference System insert ignore into journalissuearticles values(ANFIS); have been used to study the Gediz River Basin which is located in the Aegean region of western Turkey. The results varied due to the complication of the data and different parts of the study area as well as the structure of the models, over all, looking at Regression coefficient insert ignore into journalissuearticles values(R2);, Root Mean Square Error insert ignore into journalissuearticles values(RMSE); and Wilcoxon insert ignore into journalissuearticles values(WT); values, ANFIS is more accurate compared to ANN and KNN models. Conversely, according to Taylor diagram, KNN is more accurate compared to ANN and ANFIS.
Keywords : Akış Tahmini, ANFIS, ANN, KNN, Gediz Nehri Havzası, Wilcoxon