نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مهندسی عمران، دانشکده فنی‌مهندسی، واحد اراک، دانشگاه آزاد اسلامی، اراک، ایران.
2 گروه مهندسی آب، دانشکده کشاورزی، واحد اراک، دانشگاه آزاد اسلامی، اراک، ایران.
3 گروه مهندسی آب، دانشکده کشاورزی، واحد مهاباد، دانشگاه آزاد اسلامی، مهاباد، ایران.
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Nowday, machine learning models are able to make good predictions based on pattern extraction between data.In this study, neural-fuzzy network (ANFIS) was used to predict the inflow to the reservoirs of a dam namely Mahabad dam located in the northwestern part of Iran. A new Harris Hawk (HHO) optimization algorithm was also used to improve the ANFIS (HHO-ANFIS) structure. Monthly precipitation and temperature and inlet flow data to the reservoir one to three months ago were used as input parameters as 6 different input patterns. About 70% of the data were used to training and 30% to test the models. The results showed that the ANFIS model has a good accuracy in training data although, test data, its accuracy was greatly reduced. The development of the HHO-ANFIS model improved the accuracy of the prediction. The patterns with all input parameters had the highest prediction accuracy. In this pattern, values of root mean square error (RMSE), mean absolute error (MAE) and Sutcliffe Nash coefficient (NSE) for test data were 3.9 MCM, 2.41 MCM and 0.86, respectively. Due to the good performance of the model used, it can be recommended for time series predictions.
کلیدواژه‌ها [English]
)