مقایسه عملکرد مدل های استوکاستیک در تولید داده های ماهیانه جریان و بارندگی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشیار /گروه مهندسی آب، دانشگاه ارومیه

2 کارشناس ارشد سازه های آبی/ دانشگاه ارومیه

چکیده

مدل‌های تولید داده مصنوعی به عنوان ابزار مناسبی جهت پیش‌بینی و تولید سری ‌های زمانی جانشین یا یک سری بسیار طولانی مدت در مطالعات منابع آب شناخته شده و این مدل‌ها به طور وسیعی در سراسر دنیا توسط محققین مختلف مورد استفاده قرار می‌گیرد. این مدل‌ها برای تولید داده‌های مصنوعی سالیانه، ماهیانه و روزانه بارندگی یا جریان رودخانه ‏ای در طی دهه‌ های اخیر توسعه داده شده‌اند. در این میان مدل‌ های تولید داده مصنوعی ماهیانه به دلیل استفاده در مطالعات مهم و اساسی سیستم‌ های منابع آب از جمله سیستم مخازن ذخیره، پایش خشکسالی و غیره دارای اهمیت خاصی بوده و استفاده از آنها تحلیل دقیقتر از رفتار عملکرد واقعی چنین سیستم ‌هایی را فراهم می‌کند. از طرف دیگر، پایه نظری مدل‌های استوکاستیک مختلف تولید داده‌های ماهیانه متفاوت بوده و این امر می‌تواند اثر قابل توجهی در عملکرد آنها داشته باشد. لذا انتخاب یک مدل مناسب‌‌تر در مطالعه و تحلیل دقیق و درست یک سیستم منابع آب یکی از دغدغه‌های مهم متخصصین منابع آب می‌باشد. در این مطالعه با استفاده از روش شبیه سازی مونت کارلو، عملکرد چهار تیپ ازمدل غیرپارامتریک Bootstrap و مــدل‌هــــای پـــارامتـــریــک Valencia-Schaake، Thomas-Fiering و Fragment در تولید داده ‌های ماهیانه مصنوعی مورد مقایسه و ارزیابی قرار گرفته است. بدین منظور داده‌های جریان ماهیانه 47 ساله سه رودخانه نازلوچای، شهرچای و باراندوزچای واقع در استان آذربایجان غربی درشمال غرب ایران مورد استفاده قرار گرفته و 1000 سری زمانی مصنوعی جریان‌های ماهیانه 47 ساله برای هر سه رودخانه مذکور به ازاء هر کدام از مدل‌های هفتگانه مورد مطالعه، تولید و مورد استفاده قرار گرفته است. نتایج مطالعه نشان داد که مدل توزیعی Valencia-Schaake دارای عملکرد بسیار مطلوب نسبت به سایر مدل‌ها به ازاء تمامی آماره‌های مطرح ارزیابی است. 

کلیدواژه‌ها


عنوان مقاله [English]

A Comparison among the Performance of the Stochastic Models in Generating the Monthly Streamflow and Rainfall Data

نویسندگان [English]

  • M. Montaseri 1
  • J. Heydari 2
1 Associate Professor, Department of Water Engineering, Urmia University, Urmia, Iran,
2 Department of Water Engineering, Urmia University, Urmia, Iran
چکیده [English]

Synthetic data generation models have been recognized as useful tools to predict and generate alternative time series or long-term series throughout the studies conducted in the domain of water resources management. Accordingly, these models have widely been used by different researchers across the world. In the recent decades, these models have been developed to generate annual, monthly, and daily rainfall or river flow data. Among the synthetic data generated, monthly data are of great importance since they are used in the critical and important studies in the field of water resource systems, such as storage tanks and drought monitoring. Accordingly, the utilization of the monthly synthetic data models leads to more detailed analyses about the real performance of such systems. On the other hand, the theoretical basis of different stochastic models is the generation of diverse monthly data and the performance of these models can remarkably be affected by this fact. Therefore, one can argue that selecting an appropriate model is one of the major concerns of water resources experts. As such, this study made use of the Monte Carlo simulation method to compare and assess the performance of four types of non-parametric Bootstrap models as well as parametric models of Valencia-Schaake, Thomas-Fiering, and Fragment in monthly synthetic data generation. To do this, the monthly flow data of Nazluchay, Shaharchay, and Barandozchay rivers, located at the Western Azerbaijan province in the North West of Iran, were analyzed over a 47-year period. Then, 1000 synthetic time series of monthly flows for these rivers were generated and used for each of the desired seven models over a 47-year period thereof. The results indicated that, compared to other models, the Valencia-Schaake distribution model had a very high performance in terms of all well-known assessment statistical indicators.

کلیدواژه‌ها [English]

  • Synthetic data generation
  • Non-parametric models
  • Parametric models
  • Bootstrap
  • Valencia and Schaake
  • Fragment
  • Thomas-Fiering
  • Hydrological system
  • Storage systems
  • Synthetic time series
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