Yeşilırmak Nehri Akış Verisi Tahmininin Yapay Sinir Ağları Kullanılarak Karınca Aslanı Algoritması ile Zaman Gecikmesi Boyutunun Optimizasyonu

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 (KNN), Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (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 (R2), Root Mean Square Error (RMSE) and Wilcoxon (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.

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Yeşilırmak Nehri Akış Verisi Tahmininin Yapay Sinir Ağları Kullanılarak Karınca Aslanı Algoritması ile Zaman Gecikmesi Boyutunun Optimizasyonu

Cited by 3 publications

References 7 publications

Coupling machine learning with signal process techniques and particle swarm optimization for forecasting flood routing calculations in the Eastern Black Sea Basin, Türkiye

Coupling machine learning with signal process techniques and particle swarm optimization for forecasting flood routing calculations in the Eastern Black Sea Basin, Türkiye

Effect of Decision Tree in the ANFIS Models: An Example of Completing Missing Data

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

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