پیش‌بینی متغیر سرعت باد در حوضه زاینده رود با استفاده از سری زمانی

جان نثاری, فائزه; اسلامیان, سعید

doi:10.22103/nrswe.2022.19744.1007

نوع مقاله : مقاله پژوهشی

نویسندگان

¹ علوم و مهندسی آب، کشاورزی،دانشگاه صنعتی اصفهان،اصفهان،ایران

² علوم و مهندسی آب، دانشکده مهندسی کشاورزی، دانشگاه صنعتی اصفهان، ایران

10.22103/nrswe.2022.19744.1007

چکیده

باتوجه به تاثیر سرعت باد بر مسائل مهم اقلیمی همچون تبخیر و تعرق، در این تحقیق سرعت باد در هفت ایستگاه هواشناسی حوضه زاینده‌رود با استفاده از روش سری زمانی مورد بررسی قرار گرفت. دوره‌های زمانی مورد بررسی شامل 7 روز، 15 روز، 30 روز، فصلی و روزانه بود. داده‌های پرت با روش‌های نمودار جعبه‌ای، نرمال و گروبزبک مشخص گردید. مدلسازی با بررسی نمودارهای خودهمبستگی و خودهمبستگی جزئی و معیارهای آکایک، شوارتز و حنان کوین انجام شد. سپس نرمال بودن با آزمون‏های کولموگروف اسمیرنوف و جارک برا مورد بررسی قرار گرفت. برای بررسی صحت مدل از آزمون‌های دوربین واتسون و پرت مانتو استفاده شد. آزمون روند و همگنی با استفاده از نرم افزار Matlab و مدل‌سازی با استفاده از نرم افزار Minitab، Eviews انجام گردید. جهت اعتبارسنجی در بازه‌های روزانه و 7 روزه از 5 درصد داده‏ها و در 15 روز و 30 روز از 10 درصد و در بازه‌ زمانی فصلی از 20 درصد داده‏ها استفاده شد. بررسی روند با روش ناپارامتری من‏کندال انجام گرفت و تقریباً در تمامی بازه‌ها روند افزایشی مشاهده شد. . همچنین بر اساس نتایج بدست آمده (ضریب تعیین بالاتر از 7/0 و میانگین مربعات خطا و درصد خطا زیر 20درصد) مدل SARIMA در بازه‌های زمانی 7روزه و 15روزه و 30 روزه و مدل ARIMA در بازه‌های زمانی روزانه و فصلی به عنوان مدل برتر معرفی گردید.

کلیدواژه‌ها

عنوان مقاله [English]

Prediction of Wind Speed Meteorological Variable in Zayandehrud Basin Using Time Series Analysis

نویسندگان [English]

Faezeh Jannesary ¹
Saeid eslamian ²

¹ Department of Water Science and Engineering، Faculty of agricultural engineering, Isfahan university of technology

² Department of Water Science and Engineering, Faculty of agricultural engineering. Isfahan University of Technology, Isfahan, Iran

چکیده [English]

Considering the effect of wind speed on important climatic issues such as evaporation, wind speed was investigated in seven meteorological stations of Zayandehrud basin using time series method. In this study the periods includes 7 days, 15 days, 30 days, seasonal and daily. Outlier data were determined by box diagram, normal and Grubs beck methods. The modeling was performed by examining the autocorrelation and partial autocorrelation diagrams using the criteria of Akaike, Schwartz and Hannan Quinn. Kolmogorov-Smirnov and Jark Bera tests that were used to check the normality of data and Durbin Watson and Pert Manto tests were used to check the accuracy of the model. Trend and homogeneity tests were performed using Matlab and modeling was performed using Minitab, Eviews. For validation at daily and 7-day intervals, 5% of data were considered, at 15 days and 30 days, 10% of data and at seasonal intervals, 20% of data. The trend analysis was performed by non-parametric Man Kendall method and the trend was observed in the majority of intervals. also, based on the results obtained (the coefficient of determination above 0.7 and the mean square error and error percentage below 20%) the model of SARIMA was introduced as the best model in the 7-day, 15-day and 30-day time intervals, and ARIMA model in daily and seasonal intervals

کلیدواژه‌ها [English]

Forecasting
Zayandehrud basin
Time series
Wind Speed
ARIMA
SARIMA

مراجع

1.Alencar D.B., Affonso C.M., Oliveira R.C., and Jose Filho C.R. 2018. Hybrid approach combining SARIMA and neural networks for multi-step ahead wind speed forecasting in Brazil. IEEE Access, 6: 55986-55994. https://doi.org/ 1109/ACCESS.2018.2872720

2.Alsharif M.H., Younes M.K. and Kim J. 2019. Time series ARIMA model for prediction of daily and monthly average global solar radiation: Tcastud Seo. Symmetry, 11: 240-257. https://doi.org/10.3390/sym11020240

3.Asfaw A., Simane B., Hassen A. and Bantider A. 2018. Variability and time series trend analysis of rainfall and temperature in northcentral Ethiopia: A case study in Woleka sub-basin. Weather and Climate Extremes, 19:29-41. https://doi.org/10.1016/j.wace.2017.12.002

4.Chen J., Zeng G.Q., Zhou W., Du W., and Lu K.D. 2018. Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization. Energy conversion and management, 165, 681-695. https://doi.org/10.1016/j.enconman.2018.03.098

5.Dudangeh A., Abedi Koopai J., and Gohari A. 2012. Application of time series models to determine the trend of future climatic parameters in order to manage water resources. Water and Soil Science, 16: 59-74 (In Persian).

6.Eslamian S. 2014. Handbook of Engineering Hydrology Engineering Hydrology and Water Management. CRC Press, USA.

7.Gardfaramarzi S., Saberi, and Qaisouri A. 2017. Determining the best time series model in forecasting annual rainfall of selected stations in West Azerbaijan province. Applied Research in Geographical Sciences, 17: 87-105 (In Persian).

8.Helmi M., Bakhtiari B. and Ghaderi K. 2020. Modeling and forecasting of meteorological drought using SARIMA time series model in different climatic samples of Iran, Iranian Journal of Irrigation and Drainage, 14:1090-1079 (In Persian).

9.Kalamaras N., Philippopoulos K., Deligiorgi D., Tzanis C.G. and Karvounis G. 2017. Multifractal scaling properties of daily air temperature time series. Chaos, Solitons & Fractals, 98: 38-43. https://doi.org/10.1016/j.chaos.2017.03.003

10.Khazaei M., and Mirzaei M. 2014. Prediction of climatic variables using time series analysis of Zohreh watershed. Applied Research in Geographical Sciences, 34: 233-250 (In Persian).

11.Khtar B., and Bahmani A. 2015. Prediction of soil layer temperature using time series models. Journal of Soil Research (Soil and Water Sciences), 210-199 (In Persian).

12.Kocsi T., Kovács-Székely I., and Anda A. 2020. Homogeneity tests and non-parametric analyses of tendencies in precipitation time series in Keszthely, Western Hungary. Theoretical and Applied Climatology, 139 :849-859. https://link.springer.com/article/10.1007/s00704-019-03014-4

13.Little T.D. 2013. The Oxford Handbook of Quantitative Methods in Psychology, Oxford University Press.

14.Modares R., and Eslamian S. Modeling the time series of Zayandehrud river flow. Iranian Journal of Science and Technology, 30:570-567 (In Persian).

15.Nazaripour H., Karimi Z., and Sedaghat M. 2012. Hydrometeorological drought assessment based on the integrated drought index and its prediction with Markov chain in Sarbaz river basin (southeast of Iran). Journal of Soil and Water Sciences, 20: 169-151 (In Persian).

16.Nury A.H., Hasan, K. and Alam, M.J.B. 2017. Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh. Journal of King Saud University Science, 29:47-61. https://doi.org/10.1016/j.jksus.2015.12.002

17.Shabani B., Mousavi Baigi M., Jabbari Noghabi M., and Hero B. 2013. Modeling and forecasting the maximum and minimum monthly temperatures of Mashhad plain using time series models. Journal of Water and Soil, 27: 906-896 (In Persian).

18.Qahraman N., and Qarakhani, A. 2012. Evaluation of time series stochastic models in estimating evaporation from the pan (Case study: Shiraz station). Water Research in Agriculture (Soil and Water Sciences), 25: 81-75 (In Persian).

19.Yasir L ., Ma Y., Sher M., Muhammad Y., and Muhammad Atif 2020. Spatial analysis of temperature time series over the Upper Indus Basin (UIB) Pakistan. Theoretical and Applied Climatology, 139: 741-758. https://link.springer.com/article/10.1007/s00704-019-02993-8

پژوهش های نوین در مهندسی آب پایدار

پیش‌بینی متغیر سرعت باد در حوضه زاینده رود با استفاده از سری زمانی

مراجع

مراجع

دوره 1، شماره 1 - شماره پیاپی 1
شهریور 1401
صفحه 27-43

پیش‌بینی متغیر سرعت باد در حوضه زاینده رود با استفاده از سری زمانی

مراجع

مراجع

دوره 1، شماره 1 - شماره پیاپی 1شهریور 1401صفحه 27-43

دوره 1، شماره 1 - شماره پیاپی 1
شهریور 1401
صفحه 27-43