بهبود برآورد مقادیر شبیه‌سازی شده دبی رودخانه با استفاده از مدل‌های ساختاری فضای حالت

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری منابع آب، گروه هیدرولوژی و منابع آب، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران

2 دانشیار گروه هیدرولوژی و منابع آب، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران

3 گروه هیدرولوژی و منابع آب، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران.

4 گروه آمار، دانشکده علوم ریاضی و کامپیوتر، دانشگاه شهید چمران اهواز، اهواز، ایران.

چکیده

شبیه‌سازی سامانه، با ساختارهای متفاوت و با استفاده از رویکردها‌ و الگوریتم‌های مختلف صورت می‌گیرد. الگوریتم‌ها روش‌های هوشمند پردازش داده در یادگیری ماشین هستند که می‌توانند عوامل ناشناخته در یک پدیده وابسته به زمان را شناسایی نمایند. در تحلیل پدیده‌های تصادفی از جمله روش‌هایی که می‌تواند تصمیم‌گیری را ساده‌تر کند؛ استفاده از الگوریتم‌های ترکیبی است. به‌کمک این روش، مدیریت داده‌ دقیق‌تر‌ و شناخت بیشتری از سامانه مورد مطالعه بدست می‌آید. از آنجایی‌که بررسی مؤلفه روند می‌تواند در شبیه‌سازی پدیده‌های هیدرولوژیکی مؤثر باشد و در تفسیر رابطه بین فرآیندهای هیدرولوژیکی و تغییرات محیطی در مناطق مورد مطالعه کمک مؤثری نماید؛ مدل‌‌های فضای حالت این مزیت را دارند که سامانه را به‌صورت انعطاف ‌پذیر و پویا مورد بررسی و تحلیل قرار دهند. لذا این مقاله در نظر دارد به‌کمک روش ترکیبی به‌بهبود راندمان مدل‌های سری زمانی فضای حالت Kalman Filter، ETS، BATS،TBATS  بپردازد و با مقایسه با مدل باکس-جنکینز نشان دهد کدامیک از این مدل‌ها، قابلیت بهتری در شبیه‌سازی دبی ماهانه رودخانه دارد. این مقایسه در سه ایستگاه آب‌سنجی سپیددشت‌ سزار، تنگ‌پنج بختیاری و تله‌زنگ در حوضه آبریز دز واقع در استان خوزستان از سال 1386تا 1399 انجام شده است. نتایج این بررسی براساس معیار‌های ارزیابی مدل(RMSE، MAE و R2)، نشان داد فضای حالت نسبت به مدل باکس‌جنکینز (کلاسیک) بهتر عمل نموده و در بین مدل‌های فضای حالت، مدل سطح موضعی(فیلتر کالمن) عملکرد بهتری داشته، به‌طوری‌که در مرحله صحت‌سنجی، ایستگاه آب‌سنجی سپیددشت سزار 21/39 RMSE=، 79/0 R2=و در ایستگاه تنگ‌پنج بختیاری 89/57 RMSE= ،76/0R2= و در ایستگاه تله‌زنگ41/113RMSE= و 73/0R2= بدست آمد.

کلیدواژه‌ها


عنوان مقاله [English]

Improving the Estimation of Simulated River Discharge Values Using State Space Structural Models

نویسندگان [English]

  • Amin Mohammadzadeh Shobegar 1
  • mohammadreza sharifi 2
  • Fereydoon Radmanesh 3
  • Behzad Mansouri 4
1 PhD student in Water Resources, Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2 Associate Professor Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
3 Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
4 Department of Statistics, Faculty of Mathematics and Computer Science, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
چکیده [English]

System simulation is done with different structures and by using different approaches and algorithms. Algorithms are intelligent methods of data processing in machine learning that can identify unknown factors in a time-dependent phenomenon. In the analysis of random phenomena, among the methods that can make decision-making easier is the ensemble algorithms. With the help of this method, more accurate data management and more knowledge of the studied system is obtained. Since, investigation of the trend component can be effective in simulating hydrological phenomena and help in interpreting the relationship between hydrological processes and environmental changes in the study areas; State space models have the advantage of analyzing the system flexibly and dynamically. Therefore, this article aims to improve the efficiency of Kalman Filter, ETS, BATS, and TBATS state space time series models with the help of an ensemble method and by comparing with the Box-Jenkins model, to show which of these models has a better capability in simulating the monthly discharge of the river. This comparison has been done in three water measuring stations of Sepiddasht Cesar, TangPanj Bakhtiari and Telezang in Dez catchments located in Khuzestan province since 1386 to 1399. The results of this study, based on the model evaluation criteria (RMSE, MAE and R2), showed that the state space performed better than the Box-Jenkins model (classical), and among the state space models, the local level model (Kalman filter) performed better. So that in the validation stage, RMSE = 39.21and R2 = 0.79 in Sepiddasht Cesar water measuring station, RMSE = 57.89 and R2 = 0.76 in TangPanj Bakhtiari station and RMSE = 113.41 and R2= 0.73 in Telezang station were obtained.

کلیدواژه‌ها [English]

  • "time series"
  • "state space models"
  • "ensemble method"
  • "monthly discharge"
  • "Dez catchments"
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