چکیده مقاله
Streamflow forecasting is an important issue in water resource management In this paper, the application of Adaptive Neuro fuzzy Inference System ANFIS is investigated in modeling monthly and seasonal streamflow forecasts Moreover, K fold as the cross validation method is used to evaluate test training data in the model Results are compared with those of the typical method i e , using 75% of data for training and the remaining 25% for testing the validity of the trained model Study area is Taleghan basin located at northwestern Tehran, Iran The data used in this research consists of 19 years of monthly streamflow, precipitation and temperature records To apply temperature and precipitation data in the model, the whole basin was divided into sub basins and average values of each parameter for each sub basin were allocated as model input Finally, results are compared with those of the ANN model It was found that the forecasting models using K fold are more reliable In addition, the ANFIS model shows better performance than the ANN model in predicting peak flows and other model evaluation indices including the Nash Sutcliffe Efficiency Index and Scatter Index
کلیدواژهها
نویسندگان
R Esmaeelzadeh
Ph D Student, Department of Civil Engineering, Shahid Chamran University, Ahwaz, Iran
A. B. Dariane
Associate Professor, Department of Civil Engineering, K N Toosi University of Tech , Tehran
شیوه ارجاع
Esmaeelzadeh, R and Dariane, A. B.,1391,Adaptive Neuro-Fuzzy Inference System for Long-term Streamflow Forecasts Using K-fold Cross-validation: Taleghan basin, Iran,09th International River Engineering Conference,Ahvaz
ارائهشده در
مجموعه مقالات نهمین سمینار بین المللی مهندسی رودخانه3 بهمن 1391 · اهواز