پیش‌بینی رواناب با استفاده از مدل‌های جعبه سیاه و خاکستری

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار /دانشکده کشاورزی، دانشگاه گنبد کاووس-گنبد کاووس.

2 دانش آموخته کارشناسی ارشد/ آبخیزداری، دانشگاه گنبدکاووس-گنبد کاووس

3 استادیار /دانشکده کشاورزی ، دانشگاه گنبد کاووس-گنبد کاووس

4 استادیار/ دانشکده کشاورزی ، دانشگاه گنبد کاووس-گنبد کاووس

چکیده

در دهه گذشته، یادگیری ماشین یک روش مناسب برای مدل‌سازی تجربی بارش-رواناب به عنوان یک مکمل مفید برای مدل‌های هیدرولوژیکی مطرح شده است، به ویژه در حوضه‌هایی که داده‌ها برای مدل‌های داده محور محدود هستند. در این تحقیق از مدل‌های جعبه سیاه (نروفازی و ماشین بردار پشتیبان) و مدل های جعبه خاکستری (TOPMODEL و HBV) برای شبیه سازی فرآیند بارش-رواناب روزانه در حوضه نوده خاندوز که در رودخانه گرگانرود قرار دارد، استفاده شد و عملکرد آن‌ها با توجه به دقت پیش‌بینی رواناب مقایسه گردید. برای مدل های جعبه سیاه، سه سری ورودی شامل دبی، دما و بارندگی در 9 سناریوی متفاوت بر اساس داده‌های سری زمانی انتخاب گردید. مقایسه مقادیر میانگین مربعات خطا و ضریب تعیین نشان می‌دهد مدل نروفازی با دبی تا سه گام زمانی قبل و دمای گام زمانی قبل عملکرد بهتری نسبت به سایر سناریوها دارد. به طور کلی مدل‌های جعبه سیاه رواناب را در مرحله واسنجی و صحت‌سنجی با دقت بیشتری نسبت به HBV و TOPMODEL شبیه‌سازی کرده‌اند. مقایسه دقیق عملکرد کل مدل‌ها نشان داد که مدل‌های نروفازی و ماشین بردار پشتیبان رواناب را در فصل‌های گرم با دقت کمتری نسبت به فصل‌های سرد پیش بینی کرده است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Runoff prediction using black and gray box models

نویسندگان [English]

  • S. M. Seyedian 1
  • M. Bagherpour 2
  • A. Fathabadi 3
  • A. Mohammadi 4
1 Assistant Professor, Department of Agriculture, University, Ghonbad Kavous, Iran.
2 Graduate student of watershed management, Ghonbad Kavous University, Gonbad Kavous, Iran.
3 Assistant Professor, Department of Agriculture, University, Ghonbad Kavous, Iran
4 Assistant Professor, Department of Agriculture, University, Ghonbad Kavous, Iran.
چکیده [English]

In the past decade, machine learning for empirical rainfall–runoff modeling is considered to be a promising approach as a useful complement to hydrologic models, particularly in basins where data to support process-based models are limited. In this paper, we used black-box models (i.e. neuro-fuzzy and support vector machine) and gray-box models (i.e. TOPMODEL and HBV) for simulating the transformation of daily rainfall-runoff process in the Nodeh khormaloo watershed located in Gorganrood River Basin and compare their performance in terms of predictive accuracy. For the black-box models, the three input vectors including discharge, temperature and rainfall are selected in nine different scenarios based on the sequential time series data. Our result show that the neuro-fuzzy model which consists of three antecedent values of flow and one antecedent values of temperature outperforms other models when the root mean square error and coefficient of determination are used as quality indicators. In general, the black- box models outperformed the HBV and TOPMODEL simulations for the calibration and validation data sets. A detailed comparison of the overall performance indicated that the neuro-fuzzy and SVM models predicted runoff in warm months were consistently lower than that in the cold months.

کلیدواژه‌ها [English]

  • Artificial intelligence
  • Conceptual model
  • Rainfall
  • runoff
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