نوع مقاله : مقاله پژوهشی
1 گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه فردوسی مشهد
2 مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی دانشگاه تهران
3 مهندس کنترل منابع آب در هیئت مدیره کنترل منابع آب ایالتی، ساکرامنتو، کالیفرنیا، امریکا،
عنوان مقاله [English]
Rainfall-runoff monthly modelling process plays an important role in dams’ operation. Herein the performances of three data-based models including Artificial Neural Network (ANN), Generalized Regression Neural Network (GRNN) and K-Nearest Neighbor (KNN) are compared in tandem with IHACRES conceptual model, while they were applied with similar data, and optimal structures. Simulation of monthly inflow to Karkheh reservoir, Iran, was considered as the case study, and 32-year data (1982-2014) of monthly temperature and precipitation belong to the upper sub-basin of the dam, and monthly inflow to the reservoir were used. With respect to the different rainfall-runoff patterns in different months, the models assessed in a general and monthly manners using a rating method based on performance criteria including: Nash-Sutcliff Efficiency (NSE), Root Mean Square Error (RMSE) and Correlation Coefficient(R). Results showed that both model evaluation procedure in validation phase, ANN and KNN models have the highest and lowest efficiency in monthly streamflow forecasting, respectively. Based on the rating general evaluation the performance of ANN (NSE= 0.749, R= 0.868) and IHACRES (NSE= 0.699, R= 0.842) are similar with a score of 8 while the GRNN (NSE= 0.618, R= 0.793) and KNN (NSE= 0.601, R= 0.777) models with similar performance (score 5) were ranked in the second order. However, in accordance with rating monthly assessment of the models, the performance of GRNN was similar to IHACRES with the total score of 38 based on three criteria while they were ranked in the second order after ANN model with score 48.