Comparative Study Among Different Time Series Models for Monthly Rainfall Forecasting in Shiraz Synoptic Station, Iran

Bahrami, Mehdi; Mahmoudi, Mohammad Reza; Zarei, Abdol Rassoul; Moghimi, Mohammad Mehdi; Amiri, Mohammad Javad

doi:10.22077/jwhr.2019.1053

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	Comparative Study Among Different Time Series Models for Monthly Rainfall Forecasting in Shiraz Synoptic Station, Iran
Water Harvesting Research
مقاله 3، دوره 3، 1&2، فروردین 2018، صفحه 29-39 اصل مقاله (1.1 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22077/jwhr.2019.1053
نویسندگان
Mehdi Bahrami^* ¹؛ Mohammad Reza Mahmoudi²؛ Abdol Rassoul Zarei³؛ Mohammad Mehdi Moghimi¹؛ Mohammad Javad Amiri¹
¹Department of Water Engineering, Faculty of Agriculture, Fasa University, Fasa, Iran
²Department of Statistics, Faculty of Science, Fasa University, Fasa, Iran
³Department of Range and Watershed Management, Faculty of Agriculture, Fasa University, Fasa, Iran
چکیده
In this research, monthly rainfall of Shiraz synoptic station from March 1971 to February 2016 was studied using different time series models by ITSM Software. Results showed that the ARMA (1,12) model based on Hannan-Rissanen method was the best model which fitted to the data. Then, to assess the verification and accuracy of the model, the monthly rainfall for 60 months (from March 2011 to February 2016) was forecasted and compared with the observed rainfall values in this period. The determination coefficient of 99.86 percent (R²=0.9986) and positive correlation (P˂0.05) between the observed data and the predicted values by the ARMA (1,12) model illustrates the goodness of this model in prediction. Finally, based on this model, monthly rainfall values were predicted for the next 60 months that the model had not been trained. Results showed the forecasting ability of the chosen model. So, it can conclude that the ARMA (1,12) model is the best-fitted model overall.
کلیدواژه‌ها
ARMA؛ Forecasting؛ Rainfall؛ Shiraz؛ Time Series

مراجع
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. Bahrami, M., Bazrkar, S., & Zarei, A. R. (2018). Modeling, prediction and trend assessment of drought. Water and Climate Change. http://dx.doi.org/10.2166/wcc.2018.174. Bahrami, M., Tavakol Sadrabadi, M., & Zarei, A. R. (2014). Analysis of Intensity- Duration and Frequency of Drought and Trend of Precipitation Changes in the Shiraz Synoptic Station (Iran). Journal of Irrigation and Water Engineering, 6(21), 59-74. Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1994). Time series analysis: forecasting and control. Upper Saddle River, NJ. Bras, R. L., & Rodriguez-Iturbe, I. (1985). Random functions and hydrology. Addison-Wesley, Reading, MA. Brockwell, P. J., & Davis, R.A. (2010). Introduction to time series and forecasting. Springer, New York. Dastorani, M., Mirzavand, M., Dastorani, M. T., & Sadatinejad, S. J. (2016). A comparative study among different time series models applied to monthly rainfall forecasting in semi-arid climate condition. Journal of Nat Hazards, DOI: 10.1007/s11069-016-2163-x Davidson, J. W., Savic, D. A., & Walters, G. A. (2003). Symbolic and numerical regression: experiments and applications. Journal of Information Science, 150(1–2), 95–117. Dutta, D., Wendy, D., Welsh, J. V., Shaun, S. H. K., & Nicholls, D. (2012). A comparative evaluation of short-term stream ﬂow forecasting using time series analysis and rainfall-runoff models in the water source. Water Resour Manage, 26, 4397–4415. doi:10.1007/s11269-012-0151-9 Farajzadeh, J., Fakheri Fard, A., & Lotfi, S. (2014). Modeling of monthly rainfall and runoff of Urmia lake basin using “feed-forward neural network” and “time series analysis” model. Water Resources and Industry, 7(8), 38-48. https://doi.org/10.1016/j.wri.2014.10.003. Feng, Q., Wen, X., & Li, J. (2015). Wavelet analysis-support vector machine coupled models for monthly rainfall forecasting in arid regions. Water Resour Manage, 29(4), 1049–1065. Galeati, G. (1990). A comparison of parametric and non-parametric methods for runoff forecasting. Journal of Hydrological Sciences, 35(1–2), 79–94. Gorman, J. W., & Toman, R. J. (1966). Selection of variables for the fitting equation to data. Technometrics, 8(1), 27–51. Hooshmand, A. R., Salarijazi, M., Bahrami, M., Zahiri, J., & Soleimani, S. (2013) Assessment of pan evaporation changes in South-Western Iran. African Journal of Agricultural Research, 8(16), 1449-1456. Hsu, K., Gupta, H. V., & Sorooshian, S. (1995). Artificial neural network modeling of the rainfall-runoff process. Water Resources Research, 31(10), 2517–2530. Lall, U., & Bosworth, K. (1993). Multivariate kernel estimation of functions of space and time hydrologic data. In: Hipel KW (ed.). Stochastic and Statistical Methods in Hydrology and Environmental Engineering. Springer Velag, Kluwer, NewYork. Lin, G. F., & Lee, F. C. (1992). An aggregation-disaggregation approach for hydrologic time series modeling. Journal of Hydrology, 138(3–4), 543–557. doi:10.1016/0022-1694(92)90136-J Mahsin, M., Akhter, Y., & Begum, M. (2012). Modeling rainfall in Dhaka Division of Bangladesh using time series analysis. Journal of Mathematical Modelling and Application, 1(5), 67–73. Mair, A., & Fares, A. (2011). Time series analysis of daily rainfall and streamﬂow in a volcanic dike-intruded aquifer system, O‘ahu, Hawai‘i, USA. Hydrogeol J, 19, 929–944. doi:10.1007/s10040-011-0740-3 Meher, J., & Jha, R. (2013). Time-series analysis of monthly rainfall data for the Mahanadi River Basin, India. Sciences in Cold and Arid Regions, 5(1), 0073-0084. Mirzavand, M., & Ghazavi, R. (2015). A stochastic modeling technique for groundwater level forecasting in an arid environment using time series methods. Water Resour Manage. doi:10.1007/s11269-014-0875-9 Mondal, M. S., & Wasimi, S. A. (2007, March). Choice of model type in stochastic river hydrology. In: Proceedings of the 1st international conference on water and ﬂood management (ICWFM), Dhaka, Bangladesh. Radhakrishnan, P., & Dinesh, S. (2006). An alternative approach to characterize time series data: a case study on Malaysian rainfall data. Chaos Solitons Fractals, 27, 511–518. doi:10.1016/j.chaos.2005.04.030 Sadeghian, M. S., Salarijazi, M., Ahmadianfar, I., & Heydari, M. (2016). Stage-discharge relationship in tidal rivers for tidal flood condition. Fresenius Environmental Bulletin, 25(10), 4111-4117. Salari, A., Tavakol Sadrabadi, M., Zarei, A. R., & Bahrami, M. (2015). Evaluation of climate indices and the general trend of climate changes (Case study: Shiraz synoptic station). Journal of Irrigation and Water Engineering. 6(22), 138-150. Salas, J. D., Deulleur, J. W., Yevjevich, V., & Lane, W. L. (1980). Applied Modeling of Hydrologic Time Series. Water Resources Publications, Littleton, CO. Schwarz, G. (1978). Estimating the dimensions of a model. Annals of Statistics, 6(2), 461-464. Shirmohammadi, B., Vafakhah, M., Moosavi, V., & Moghaddamnia, A. (2013). Application of several data-driven techniques for predicting groundwater level. Water Resour Manag, 27, 419–432. doi:10.1007/s11269012-0194-y Soltani, S., Modarres, R., & Eslamian, S. S. (2007). The use of time series modeling for the determination of rainfall climates of Iran. Int J Climatol, 27, 819–829. doi:10.1002/joc.1427 Weesakul, U., & Lowanichchai, S. (2005). Rainfall forecast for agricultural water allocation planning in Thailand. Thammasat International Journal of Science and Technology, 10(3), 18–27. Willems, P. (2009). A time series tool to support the multi-criteria performance evaluation of rainfall-runoff models. Environ Model Softw, 24, 311–321. doi:10.1016/j.envsoft.2008.09.005.
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Comparative Study Among Different Time Series Models for Monthly Rainfall Forecasting in Shiraz Synoptic Station, Iran