کاربرد مدل سری زمانی برای تخمین میزان بارش ماهانه در استان کرمانشاه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش آموخته آمار ریاضی، دانشگاه رازی، کرمانشاه

2 دانشجوی دکترای سازه‌های آبی دانشگاه رازی، کرمانشاه

چکیده

پیش بینی فرایندهای آب و هوایی ابزار مناسبی در اختیار مدیران حوضه های مختلف قرار می دهد، تا با در نظر گرفتن این پیش بینی ها، سیاست‌های آینده را در جهت بهینه نمودن صرف هزینه ها و امکانات بهره وری حداکثر طرح ریزی کنند. پیش بینی بارش برای اهداف مختلفی نظیر برآورد سیلاب، خشکسالی، مدیریت حوضه آبریز، کشاورزی و ... دارای اهمیت بسیاری است. در این تحقیق، جامعه آماری شامل میزان بارش در ایستگاه­های سینوپتیکی­ استان کرمانشاه، کنگاور، سرپل ذهاب و اسلام آباد غرب می­باشد. روش مطالعه به صورت مقطعی و حجم نمونه نیز تمام داده­های میزان بارش طی سال­های 1365 تا 1397 می­باشد. به منظور تجزیه و تحلیل داده­ها از روش آریما برای برازش مدل­سازی سری زمانی و در انتها بعد از آزمون مدل­های موجود بهترین مدل برای پیش بینی میزان بارش تعیین گردید. نتایج بررسی­ها نشان داد که مدل سری زمانی آریما بهترین کارایی را داشته و روند کاهشی بارش به اندازه 2/0 را خواهد داشت. در بررسی­های حاضر با استفاده از داده­های 32 ساله (97-65) ایستگاه کرمانشاه، اسلام آباد، کنگاور و سرپل ذهاب و همچنین مدل­های سری زمانی اقدام به مدلسازی و پیش بینی بارش گردید. براساس نتایج بدست آمده از نمودارهای خود همبستگی و خود همبستگی جزیی، بهترین مدل برازش شده بر داده­ها مدل بود. در نهایت با توجه به تصادفی بودن و همچنین تاخیر زمانی خارج از محدوده صفر براساس باقیمانده خود همبستگی جزیی و باقیمانده خود همبستگی در مدل پیش بینی داده­ها کمتر از 05/0 می­باشد. پس مدل پیش بینی قابل اطمینان برآورد شد. و براساس مدل برازش شده بارش به اندازه 2/0 روند کاهشی را خواهد داشت.

کلیدواژه‌ها


عنوان مقاله [English]

Application of time series model to estimate monthly rainfall in Kermanshah province

نویسندگان [English]

  • leila teymouri yeganeh 1
  • maryam teymouri yeganeh 2
1 Razi univercity
2 Razi univercity
چکیده [English]

Introduction:
In recent years, limited water resources to supply water for agricultural and non-agricultural needs have caused many problems and rain is one of the important sources of water supply. On the other hand, rainfall is one of the most important components of input to hydrological systems that its study and measurement in most cases is necessary for studies of runoff, drought, groundwater, flood, sediment, etc. Therefore, forecasting and estimating rainfall for each region and watershed is considered as one of the important climatic parameters in the optimal use of water resources. One of the methods of estimating and predicting precipitation is the use of time series.
Materials and methods:
In this study, the statistical population includes the amount of precipitation in synoptic stations of Kermanshah, Kangavar, Sarpole-Zahab and Islamabad -Gharb provinces. The data has been prepared from the meteorological website at www.kermanshahmet.ir. The study method is cross-sectional and the sample size is all rainfall data during the years 1986 to 2018. In order to analyze the data in this study, spss16 and minitab18 statistical software for time series modeling fitting and finally after testing the existing models, the best model for predicting precipitation was determined.
Results and discussion:
In order to analyze the data from Arima method for fitting time series modeling and finally after testing the existing models, the best model for predicting precipitation was determined. The results showed that Arima time series model has the best performance and will have a decreasing trend of precipitation by 0.2. In the present studies, using 32-year data (1986-2018) of Kermanshah, Islamabad, Kangavar and Sarpole-Zahab stations as well as time series models, precipitation was modeled and predicted. Based on the results of autocorrelation and partial autocorrelation diagrams, the best model fitted to the data was the model Arima(2,1,1). Finally, due to randomness and time delay outside the range of zero based on partial autocorrelation residual and residual autocorrelation in the data prediction model is less than 0.05. The model was then estimated to be reliable. And according to the fitted model, precipitation will have a decreasing trend of 0.2.
Conclusion:
The analysis of random phenomena in the realm of statistics and probability is a subset of hydrology and meteorology. Due to the fact that meteorological processes are random, so the basis for the analysis of these phenomena is meteorology, statistics and probability. Accordingly, time series are used. It is natural that the existence of appropriate statistical data in the study area as input to models in processing problems and receiving reliable outputs is very important and effective. In the present studies, using 32-year data (1986-2018) of Kermanshah, Islamabad, Kangavar and Sarpole-Zahab stations as well as time series models, precipitation was modeled and predicted in the software minitab18.Based on the results obtained from the autocorrelation and partial autocorrelation diagrams, the best fit model on the data was the model Arima(2,1,1). Finally, due to randomness and time delay (Lag-time) outside the range of zero based on the residual of the partial autocorrelation function (PACF ) and the residual of the autocorrelation function (ACF ) in the data prediction model is less than 0.05, so the model Reliable forecast was estimated and according to the fitted model, precipitation will decrease by 0.2.

کلیدواژه‌ها [English]

  • Monthly Rainfall
  • time series
  • Kermanshah
  • forecast
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