تحلیل عدم قطعیت مدل های شبکه عصبی و نروفازی در پیش بینی جریان رودخانه

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری /سازه های آبی، دانشگاه تربیت مدرس

2 دانشیار/گروه سازه های آبی، دانشگاه تربیت مدرس

چکیده

پیش بینی آورد رودخانه در مدیریت منابع آب از اهمیت فراوانی برخوردار است، اما به دلیل عدم قطعیت بالا در عواملی که فرآیند بارش- رواناب را سبب می‌گردند، همواره با مشکلاتی همراه بوده است. یکی از روش‌هایی که می‌تواند این مشکل را تا حدی کاهش دهد، تحلیل‌ عدم قطعیت پیش‌بینی‌های انجام شده می‌باشد. این تحلیل‌ها در مدل‌های آماری سابقه طولانی دارند، ولی برای مدل‌های شبکه عصبی و نروفازی کمتر مورد استفاده قرار گرفته و این در شرایطی است که در سال‌های اخیر به مراتب توجه بیشتری به این تکنیک‌ها شده است. در مطالعه حاضر عدم قطعیت نتایج مدل‌های شبکه عصبی و نروفازی در پیش‌بینی 1 تا 3 ماه آینده جریان رودخانه مورد ارزیابی قرار گرفته و منطقه مطالعاتی رودخانه صوفی‌چای در محل ایستگاه تازه‌کند در نظر گرفته شده است. نتایج نشان می‌دهد که مدل نروفازی از دقت بالاتر در پیش‌بینی و عدم قطعیت کمتری برخوردار است، اما بطور مشخص برای پرآبی‌ها در هر دو مدل عدم قطعیت بیشتر شده که خطرپذیری (ریسک) کاربرد نتایج را افزایش می‌دهد.

کلیدواژه‌ها


عنوان مقاله [English]

Uncertainty Analysis of Artificial Neural Networks and Neuro-Fuzzy Models in River Flow Forecasting

نویسندگان [English]

  • A Farokhnia 1
  • S Morid 2
1 Ph.D. Student, Dep. of Water Structures Eng., Tarbiat Modares University, Tehran, Iran
2 Associate Professor, Dep. of Water Structures Eng., Tarbiat Modares University, Tehran, Iran
چکیده [English]

River flow forecasting in water resources management is of great importance. But, due to the high uncertainty in the factors affecting the rainfall-runoff process, the results are usually problematic. One of the procedures that can alleviate this problem is incorporating uncertainty analysis in forecasted results. Such an analysis has been traditionally used for statistical methods but less attention has been given to the Artificial Neural Networks (ANNs) and the Neuro-Fuzzy (ANFIS) models. These models have gained much popularity in recent years. This research has aimed to analyze the uncertainty of these techniques for 1 to 3 months forecasting of river flow. Sofy-Chay River  at Tazekand gauging station in the northwest of Iran is selected as the study site to explore the methodology. The results show that ANFIS overall gave more accurate forecasts and less uncertainty. But, when it comes to high flows, the confidence interval for the two models increases quite obviously and this increases the risk for application of the results.

کلیدواژه‌ها [English]

  • River Flow Forecasting
  • Uncertainty
  • Artificial Neural Network
  • Neuro-Fuzzy

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