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Volume 17, Number 44 (6-2017)                   researches in Geographical Sciences 2017, 17(44): 87-105 | Back to browse issues page


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Soltani gerdfaramarzi S, Saberi A, Gheisouri M. Determination of the best time series model for forecasting annual rainfall of selected stations of Western Azerbaijan province. researches in Geographical Sciences. 2017; 17 (44) :87-105
URL: http://jgs.khu.ac.ir/article-1-2751-en.html

1- Assistant Professor , ssoltani@ardakan.ac.ir
2- Master science student, Department of Watershed Management
3- Master science student
Abstract:   (464 Views)

Rainfall is one of the most important components of the water cycle and plays a very important role in the measurement of climate characteristic in any area. Limitations such as lack of sufficient information about the amount of rainfall in time and space scale and complexity of the relationship between meteorological elements related to rainfall, causes the calculation of these parameters using the conventional method not to be implemented. One method of evaluating and forecasting of rainfall in each region is time series models. In this research, to predict the average annual rainfall synoptic station at Mahabad, Uromiya and Mako in West Azarbayejan provience during 1984-2013, linear time series ARIMA was used. To investigate model static, Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) was applied and with differencing method, the non-static data transformed to static data. In next step, stochastic models to estimate the annual rainfall average were used. With regard to the evaluation criterion such as T, P-VALUE < 0.05 and Bayesian Information Creterion (BIC), ARIMA (1,0,0), ARIMA (0,1,1) and ARIMA (0,1,1) models was determined as a suitable model for predicting annual rainfall in the three selected stations at Uromiya, Makoo and Mahabad. In the following, the annual rainfall for 3 (2013-2016) years is forecasted which based on rainfall data in that time, the adjusted model was acceptable.

Full-Text [PDF 756 kb]   (374 Downloads)    
Type of Study: Research | Subject: Special
Received: 2017/06/19 | Accepted: 2017/06/19 | Published: 2017/06/19

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