Shima Kabiri - Water Structures, MS. Qazvin Regional Water Authority, Qazvin, Iran
Masoumeh Alsadat Hashemi Tameh - Irrigation and Drainage Engineering, Ph.D. Candidate. Faculty of Engineering and Technology, Imam Khomeini International University, Qazvin, Iran
Reza Ekhlsmand - Civil Engineering, Ph.D. Candidate. Qazvin Regional Water Authority, Qazvin, Iran

Time series prediction of meteorological parameters plays an important role in making decision to decrease the effects of drought and climate change. Temperature is an important factor in planning andmaking decision of water resources and water balancing. Therefore, precise estimation of temperature isindispensable for every computation in hydrology and other disciplines. There are a lot of methods for estimating time series climate data and all of them can be grouped in (A) statistical methods and (B) intelligent methods. In this study, Artificial Neural Network (ANN) and ANFIS were applied asintelligent methods to estimate the maximum and minimum temperature. The data was spited into two parts (A) 90% data that was used as training and (B) the rest of the data set which was applied as the test set to validate the constructed model. The performance of Multi Layer Perceptron and Neuro-FuzzyInference System with the fuzzy c-means clustering (FCM-ANFIS) were investigated using differentnumbers of neurons in hidden layers and different number of clustering , respectively. Accuracies of the models were evaluated using indices such as R2, RMSE andMAE.

Time series, Artificial neural network, Maximum temperature, Minimum temperature