Long Term River Flow Forecasting using Adaptive Neuro-fuzzy Inference System (ANFIS)

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Özkan F., Haznedar B.

International Conference on Advanced Technologies (ICAT’22) E-, Van, Türkiye, 25 - 27 Kasım 2022, ss.1-5

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Van
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.1-5
  • Erciyes Üniversitesi Adresli: Hayır

Özet

Water resources are one of the most basic needs of living life. In order to sustain human life without any problems, a rational planning is required for the protection and use of existing water resources. Therefore, river flow estimation is necessary to provide basic information on a wide variety of problems associated with the functioning of river systems. In this study, the daily flow values of Zamanti River-Değirmenocağı, Zamanti River-Ergenuşağı and Eğlence River-Eğribük stations in the Seyhan Basin in Turkey were investigated. Within the scope of the study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) model was trained using Back Propagation (BP) and Hybrid Learning (HB) algorithms in order to make forward flow rate estimation from past flow measurement values and the results obtained from all models were compared. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Determination Coefficient (R2) and Mean Absolute Percentage Error (MAPE) evaluation criteria were used for comparison. After the analysis, it was concluded that BP algorithm can be used more successfully and effectively than HB algorithm