Determination of the best time series model for forecasting annual rainfall of selected stations of Western Azerbaijan province
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 bet...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | fas |
| Published: |
Kharazmi University
2017-03-01
|
| Series: | تحقیقات کاربردی علوم جغرافیایی |
| Subjects: | |
| Online Access: | http://jgs.khu.ac.ir/article-1-2751-en.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832575787875696640 |
|---|---|
| author | Somayeh Soltani gerdfaramarzi Aref Saberi Morteza Gheisouri |
| author_facet | Somayeh Soltani gerdfaramarzi Aref Saberi Morteza Gheisouri |
| author_sort | Somayeh Soltani gerdfaramarzi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5d5846a0d55341649c8df7c32a4197cc |
| institution | Kabale University |
| issn | 2228-7736 2588-5138 |
| language | fas |
| publishDate | 2017-03-01 |
| publisher | Kharazmi University |
| record_format | Article |
| series | تحقیقات کاربردی علوم جغرافیایی |
| spelling | doaj-art-5d5846a0d55341649c8df7c32a4197cc2025-01-31T17:23:33ZfasKharazmi Universityتحقیقات کاربردی علوم جغرافیایی2228-77362588-51382017-03-01174487105Determination of the best time series model for forecasting annual rainfall of selected stations of Western Azerbaijan provinceSomayeh Soltani gerdfaramarzi0Aref Saberi1Morteza Gheisouri2 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.http://jgs.khu.ac.ir/article-1-2751-en.pdfpredictionautocorrelationannual rainfalltime seriesarima. |
| spellingShingle | Somayeh Soltani gerdfaramarzi Aref Saberi Morteza Gheisouri Determination of the best time series model for forecasting annual rainfall of selected stations of Western Azerbaijan province تحقیقات کاربردی علوم جغرافیایی prediction autocorrelation annual rainfall time series arima. |
| title | Determination of the best time series model for forecasting annual rainfall of selected stations of Western Azerbaijan province |
| title_full | Determination of the best time series model for forecasting annual rainfall of selected stations of Western Azerbaijan province |
| title_fullStr | Determination of the best time series model for forecasting annual rainfall of selected stations of Western Azerbaijan province |
| title_full_unstemmed | Determination of the best time series model for forecasting annual rainfall of selected stations of Western Azerbaijan province |
| title_short | Determination of the best time series model for forecasting annual rainfall of selected stations of Western Azerbaijan province |
| title_sort | determination of the best time series model for forecasting annual rainfall of selected stations of western azerbaijan province |
| topic | prediction autocorrelation annual rainfall time series arima. |
| url | http://jgs.khu.ac.ir/article-1-2751-en.pdf |
| work_keys_str_mv | AT somayehsoltanigerdfaramarzi determinationofthebesttimeseriesmodelforforecastingannualrainfallofselectedstationsofwesternazerbaijanprovince AT arefsaberi determinationofthebesttimeseriesmodelforforecastingannualrainfallofselectedstationsofwesternazerbaijanprovince AT mortezagheisouri determinationofthebesttimeseriesmodelforforecastingannualrainfallofselectedstationsofwesternazerbaijanprovince |