فصلنامه تحقیقات جغرافیایی
سال بیست و هفتم شماره 4 (پیاپی 107، زمستان 1391)

Statistical methods are one of the useful tools for climatic elements and their behaviors analysis، as well as، modeling and predicting them subtly. In this paper the behavior of time series of annual temperature of zanjan during 1956-2005 have been studied. So، based on autocorrelation، correlation coefficient، and Spearman and Mann - Kendall ways، the data trend was determined. The amount of the annual trend based on the least error squares have been estimated about -0. 04◦C، and based on line equation of regression the direction of the modeling of the trend has been shown. Based on spectrum analysis the significant harmonics of temperature through 95 percent of Interval Confidence have been examined. The cycles of 55 year that reflect trend and the cycles of 2. 3 - 2. 7 years has been shown in temperature of Zanjan. To predicate annual temperature of Zanjan، ARIMA modeling has been used. Two initial models، ARIMA (0، 1، 1) and ARIMA (1، 1، 2) have been chosen. With regard to residue testing، as well as AIC criteria Model، the ARIMA (1، 1، 2)، was found suitable. According to this model the temperature has been predicted for next 20 years. More over Markov chain model were applied on monthly temperature of Zanjan during 1956-2005. Monthly temperature data have followed the two- state Markov chain، so the probability occurrence of a warm month at Zanjan is 0. 1607 and probability occurrence of cold months is 0. 8393. The return period of warm months is about 6 months and return period of cold months is about one month.

One of the techniques that has been used in different areas of sciences، and probably would be able to simulate complicated process of rainfall-run off is artificial neural network (ANN). The purpose of this research is to evaluate the applicability of ANN in rainfall- run off simulation and the comparison of the results to those produced by HEC-HMS model in Azam river basin of Herat in Yazd province. The data used in this study are daily rainfall and daily discharge as well as instantaneous peak discharge measured in a time period of 24 years (1982-2006). At first، preparation of rainfall data and the related run off hydrographs of some events was the basic of work، then artificial neural network with error back propagation algorithm and the sigmoid transfer function was trained using the prepared data. The criterion to select and set the network parameters was the production of the least amount (RMSE) value between outputs. HMS model using SCS suggestive procedure (using CN) was also calibrated and used. To evaluate the efficiency of the models، observations and models outputs related to whole flow data، run off volume، the time to peak، and the peak discharge were compared. Findings of this research show that the correlation coefficient (r) between measured and estimated flow data is 0. 978 and 0. 823 respectively for ANN and HEC-RAS models، showing higher accuracy of ANN outputs. About the run off volume and peak discharge، r obtained respectively 0. 986 and 0. 981 for ANN where for the HEC-HMS model these are respectively 0. 979 and 0. 972. Comparing the estimated time to peak to the observed values shows more efficiency of ANN model over the HEC-HMS model as the r for outputs of these to models are respectively 0. 833 and 0. 491. In overall، comparing the application of ANN and HEC-HMS models indicates that for all evaluated parameters the efficiency of ANN is higher than HEC-HMS. However، using t-test it was observed that statistically there is no meaningful difference between the observed and estimated values at the levels of 99 and 95 percents for all parameters.