prediction of monthly precipitation based on large-scale climate signals using intelligent models and multiple linear regression (case study: semnan synoptic station)

large-scale climatic signals including ocean-atmosphere interactions, are the main factors influencing the earth’s climatic oscillations and are the most important indices in predicting of climate variables. in this research, precipitation in the next month was predicted by applying artificial neural network (ann), neuro-fuzzy network (nfn), and multiple linear regression (mlr) in semnan synoptic station. for this purpose, monthly series of precipitation of semnan synoptic station and signals of large-scale climate signals were used during a period of 45 years (1966–2010). from 540 monthly time series, the first 80% was used for training and the other 20% for testing. performance of the models was compared by using correlation coefficient (r), mean absolute error (mae), and root mean square error (rmse) criteria. results of the validation step showed that the obtained correlation coefficients (0.829, 0.793 and 0.767) are related to ann, anfis and mlr models. based on the results of this study, the next month’s precipitation of semnan synoptic station could be predicted by ann, nfn and mlr models, respectively.