بررسی عدم‌قطعیت مدل‌های داده‌مبنا در پیش‌بینی دبی ماهانه حبله‌رود

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آب، دانشکده علوم کشاورزی، دانشگاه گیلان، رشت، ایران

2 گروه مهندسی آب، دانشکده علوم کشاورزی دانشگاه گیلان، رشت، ایران

3 مربی- گروه مهندسی آب - دانشگاه گیلان

چکیده

مدیریت مناسب حوضه‌های آبریز نیازمند در اختیار داشتن پیش‌بینی‌های دقیق و قابل اطمینان از دبی رودخانه‌هاست. در سالیان اخیر، مدل‌های داده‌مبنا و به‌ویژه مدل‌های مبتنی بر هوش مصنوعی، در زمینه‌های مختلفِ مرتبط با منابع آب با موفقیت مورد استفاده قرار گرفته‌اند. با این وجود، تحلیل عدم‌قطعیت این مدل‌ها کمتر مورد توجه قرار گرفته است. در مطالعه حاضر، عدم‌قطعیت خروجی پنج مدل مبتنی بر هوش مصنوعی شامل مدل‌هایی از نوع ماژولار، PCA، TLRN، ANFIS و SVM در پیش‌بینی دبی ماهانه حبله‌رود، با استفاده از کمیت‌های 95PPU، p-factor و d-factor مورد بررسی قرار گرفته است. با استفاده از داده‌های ثبت‌شده از متغیرهای هواشناسی و دبی طی سال‌های 2012-1998 در حوضه آبریز حبله‌رود در شرق استان تهران، ساختارهای متفاوتی از مدل‌ها مورد آموزش و آزمون قرار گرفتند. مقادیر نهاییِ p-factor و d-factor برای هر کدام از پنج مدلِ مورد بررسی محاسبه شد. نتایج نشان داد SVM با p-factor نهاییِ معادل با 82 درصد در مرحله آزمون، قابل‌اعتمادترین مدل برای پیش‌بینی دبی ماهانه در حوضه مورد بررسی است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Investigating the Uncertainty of Data-Based Models in Forecasting Monthly Flow of the Hablehroud River

نویسندگان [English]

  • Jaber Salehpoor Laghani 1
  • Afhsin Ashrafzadeh 2
  • Sayed Ali Moussavi 3
1 Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
2 Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
3 Lecturer, Department of Water Engineering, University of Guilan
چکیده [English]

Accurate and reliable forecasts of river flow are required for proper management of watershed systems. In recent years, data-driven models and especially artificial intelligent based models have been successfully used in various areas related to water resources. However, uncertainty analysis of these models has been less appreciated in prior studies. In the present study, the output uncertainty of five data-driven models including modular, PCA (Principle Component Analysis), TLRN (Time-Lagged Recurrent Network), ANFIS (Adaptive-Network-based Fuzzy Inference System) and SVM (Support Vector Machine) type models in forecasting river flow has been investigated using 95PPU, p-factor and d-factor quantities. Using the observed meteorological and flow data during 1998-2012 in Hablehroud Basin, different structures of the proposed models were trained and tested. The final values of p-factor and d-factor for each model type were obtained. The results showed that SVM with a p-factor of 82% produces the most reliable forecasts in the present study.

کلیدواژه‌ها [English]

  • Uncertainty
  • monthly streamflow
  • stochastic calibration
  • Neuro-Fuzzy Model
  • Gamma test
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