Modelling the Rainfall-Runoff Data of Susurluk Basin
Yükleniyor...
Dosyalar
Tarih
2010
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
PERGAMON-ELSEVIER SCIENCE LTD
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In this study, rainfall runoff relationship was tried to be set up by using Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Interference Systems (ANFIS) models at Flow Observation Stations (FOS) on seven streams where runoff measurement has been made for long years in Susurluk Basin. A part of runoff data was used for training of ANN and ANFIS models and the other part was used to test the performance of the models. The performance comparison of the models was made with decisiveness coefficient (R(2)) and Root Mean Squared Errors (RMSE) values. In addition to this, a comparison of ANN and ANFIS with traditional methods was made by setting up Multi-regressional (MR) model. Except some stations, acceptable results such as R(2) value for ANN model and R(2) value for ANFIS model were obtained as 0.7587 and 0.8005, respectively. The high values of predicted errors, belonging to peak values at stations where multi variable flow is seen, affected R(2) and RMSE values negatively.
Açıklama
Anahtar Kelimeler
Modelling of rainfall-runoff, Artificial Neural Networks, Neuro fuzzy, Susurluk Basin
Kaynak
Expert Systems With Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
37
Sayı
9
Künye
Dorum, A., Yarar, A., Sevimli, M. F., Onüçyıdız, M., (2010). Modelling the Rainfall-Runoff Data of Susurluk Basin. Expert Systems with Applications, 37(9), 6587-6593. doi: org/10.1016/j.eswa.2010.02.127.
