ارزیابی عملکرد مدل‌های سری زمانی خطی ARMA و غیرخطی آستانه TAR در مدل‌سازی دبی روزانه

نوع مقاله : مقالات پژوهشی


1 دانشگاه تبریز

2 پردیس فنی و مهندسی شهید عباسپور، دانشگاه شهید بهشتی


مدل‌های سری زمانی از ابزارهای مهم در مدل‌سازی و پیش‌بینی فرآیندهای هیدرولوژیکی است که به منظور طراحی و مدیریت علمی پروژه‌های منابع آب به کار می‌روند. در این تحقیق به منظور مدل‌سازی میانگین دبی روزانه 6 ایستگاه آب‌سنجی واقع در بالادست سد زرینه‌رود از مدل‌های خطی خودهمبسته میانگین متحرک (ARMA) و غیرخطی خودهمبسته آستانه (TAR) 2 و 3 رژیمی استفاده شده است. به دلیل اینکه داده‌ها دارای نوسانات فصلی می‌باشند، در ابتدا داده‌های دبی روزانه برای یک دوره 15 ساله (2011-1997)، با استفاده از سری فوریه و برآورد شاخص‌های آماری نظیر میانگین و انحراف استاندارد، استاندارد شدند. سپس، داده‌های استاندارد شده برای یک دوره 13 ساله (2009-1997) واسنجی و یک دوره 2 ساله (2011-2010) صحت‌سنجی شدند. در نهایت، مدل‌های خطی و غیرخطی مناسب با استفاده از معیارهای آکائیکه و آزمون استقلال باقیمانده‌های مدل (Ljung-Box) انتخاب شدند. نتایج این تحقیق نشان داده است که بر اساس معیارهای ارزیابی، عملکرد مدل‌های‌ غیرخطی آستانه 2 و 3 رژیمی برای همه ایستگاه‌ها دارای برتری نسبت به مدل خطی در مدل‌سازی جریان روزانه رودخانه‌های بالادست سد زرینه رود می‌باشد. همچنین مدل‌سازی و مقایسه مدل‌های غیرخطی آستانه نشان داد که مدل غیرخطی 3 رژیمی دارای معیارهای ارزیابی مناسب‌تری نسبت به مدل 2 رژیمی می‌باشد.


عنوان مقاله [English]

Performance Evaluation of Linear (ARMA) and Threshold Nonlinear (TAR) Time Series Models in Daily River Flow Modeling (Case Study: Upstream Basin Rivers of Zarrineh Roud Dam)

نویسندگان [English]

  • Farshad Fathian 1
  • Ahmad Fakheri-Fard 1
  • Yagob Dinpashoh 1
  • Seyed Saeid Mousavi Nadoushani 2
1 University of Tabriz
2 Abbaspour School of Engineering, Shahid Beheshti University
چکیده [English]

Introduction: Time series models are generally categorized as a data-driven method or mathematically-based method. These models are known as one of the most important tools in modeling and forecasting of hydrological processes, which are used to design and scientific management of water resources projects. On the other hand, a better understanding of the river flow process is vital for appropriate streamflow modeling and forecasting. One of the main concerns of hydrological time series modeling is whether the hydrologic variable is governed by the linear or nonlinear models through time. Although the linear time series models have been widely applied in hydrology research, there has been some recent increasing interest in the application of nonlinear time series approaches. The threshold autoregressive (TAR) method is frequently applied in modeling the mean (first order moment) of financial and economic time series. Thise type of the model has not received considerable attention yet from the hydrological community. The main purposes of this paper are to analyze and to discuss stochastic modeling of daily river flow time series of the study area using linear (such as ARMA: autoregressive integrated moving average) and non-linear (such as two- and three- regime TAR) models.
Material and Methods: The study area has constituted itself of four sub-basins namely, Saghez Chai, Jighato Chai, Khorkhoreh Chai and Sarogh Chai from west to east, respectively, which discharge water into the Zarrineh Roud dam reservoir. River flow time series of 6 hydro-gauge stations located on upstream basin rivers of Zarrineh Roud dam (located in the southern part of Urmia Lake basin) were considered to model purposes. All the data series used here to start from January 1, 1997, and ends until December 31, 2011. In this study, the daily river flow data from January 01 1997 to December 31 2009 (13 years) were chosen for calibration and data for January 01 2010 to December 31 2011 (2 years) were chosen for validation, subjectively. As data have seasonal cycles, statistical indices (such as mean and standard deviation) of daily discharge were estimated using Fourier series. Then ARMA and two- and three-regime SETAR models applied to the standardized daily river flow time series. Some performance criteria were used to evaluate the models accuracy. In other words, in this paper, linear and non-linear models such as ARMA and two- and three-regime SETAR models were fitted to observed river flows. The parameters associated to the models, e.g. the threshold value for the SETAR model was estimated. Finally, the fitted linear and non-linear models were selected using the Akaike Information Criterion (AIC), Root Mean Square (RMSE) and Sum of Squared Residuals (SSR) criteria. In order to check the adequacy of the fitted models the Ljung-Box test was used.
Results and Discussion: To a certain degree the result of the river flow data of study area indicates that the threshold models may be appropriate for modeling and forecasting the streamflows of rivers located in the upstream part of Zarrineh Roud dam. According to the obtained evaluation criteria of fitted models, it can be concluded the performance of two- and three- regime SETAR models are slightly better than the ARMA model in all selected stations. As well as, modeling and comparison of SETAR models showed that the three-regime SETAR model have evaluation criteria better than two-regime SETAR model in all stations except Ghabghablou station.
Conclusion: In the present study, we attempted to model daily streamflows of Zarrineh Rood Basin Rivers located in the south of Urmia Lake by applying ARMA and two- and three-regime SETAR models. This is mainly because very few efforts and rather less attention have been paid to this non-linear approach in hydrology and water resources engineering generally.
Therefore, two types of data-driven models were used for modeling and forecasting daily streamflow: (i) deseasonalized ARMA-type model, and (ii) Threshold Autoregressive model, including Self-Existing TAR (SETAR) model. Each ARMA and SETAR models were fitted to daily streamflow time series of the rivers located in the study area. In general, it can be concluded that the overall performance of SETAR model is slightly better than ARMA model. Furthermore, SETAR model is very similar AR model, therefor, it can be easily used in water resources engineering field. On the other hand, due to apply these non-linear models, the number of estimated parameters in comparison with linear models has decreased.

کلیدواژه‌ها [English]

  • forecasting
  • Urmia Lake
  • Hydrological processes
  • Two- and three-regime models
  • Evaluation criteria
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