تحلیل دقت و عدم قطعیت مدل‌های هوشمند در پیش‌بینی ضریب انتشار طولی رودخانه‌ها

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار، معاونت مؤسسه تحقیقات آب، وزارت نیرو، تهران

2 مؤسسه تحقیقات آب، وزارت نیرو، دانشجوی دکترای مهندسی محیط زیست، دانشکده محیط زیست، دانشگاه تهران

3 کارشناس مؤسسه تحقیقات آب، وزارت نیرو، دانشجوی دکترای مهندسی سازه‌های آبی، دانشگاه تربیت مدرس، تهران

4 مدیرعامل شرکت عمران زیست آزما (CELCO)، دانشجوی دکترای مهندسی محیط زیست، دانشکده محیط زیست، دانشگاه تهران

5 دانشجوی دکترای مهندسی محیط‌زیست، دانشکده محیط‌زیست، دانشگاه‌ تهران

چکیده

پیش‌بینی دقیق ضریب انتشار طولی در رودخانه‌های طبیعی تا حد بسیار زیادی در تعیین توزیع غلظت آلاینده‌ها در چنین محیط‌هایی مؤثر است. عدم قطعیت موجود در نتایج به‌دست آمده از مدل‌های پیش‌بینی می‌تواند در تصمیم‌گیری‌های مناسب برای برخورد با مواد آلاینده در رودخانه‌ها تأثیر منفی داشته باشد. به‌همین دلیل، تحلیل و تعیین عدم قطعیت مدل‌های مورد استفاده برای پیش‌بینی این پارامتر بسیار مفید است. در این تحقیق با توجه به اهمیت این امر، با استفاده از مدل‌های شبکه عصبی (ANN) و نروفازی تطبیقی (ANFIS)، ابتدا مدل مناسب برای پیش‌بینی ضریب انتشار طولی در رودخانه‌های طبیعی ارائه گردید و در ادامه تحلیل عدم قطعیت دو مدل مذکور بر مبنای روش مونت-کارلو انجام شد. برای این منظور از اطلاعات هیدرولیکی و هندسه جریان استفاده گردید. نتایج این تحقیق بیانگر این مطلب بود که اگرچه مدل ANN در پیش‌بینی ضریب انتشار طولی دارای عملکرد خوبی است، اما نتایج این مدل با عدم قطعیت زیادی همراه است. با مقایسه نتایج به‌دست آمده از تحلیل عدم قطعیت دو مدل ANN و ANFIS مشخص گردید که مدلANFIS  نسبت به مدل ANN از عدم قطعیت کمتری برخوردار است و از این لحاظ بر مدل ANN برتری دارد.

کلیدواژه‌ها


عنوان مقاله [English]

Accuracy and Uncertainty Analysis of Intelligent Techniques for Predicting the Longitudinal Dispersion Coefficient in Rivers

نویسندگان [English]

  • Abbas Akbarzadeh 1
  • Roohollah Noori 2
  • Ashkan Farokhnia 3
  • Amir Khakpour 4
  • Mohammad Salman Sabahi 5
1 Assist. Prof., Water Research Institute, Ministry of Energy, Tehran
2 Member of Water Research Institute, Ministry of Energy, Ph.D. Student of Environmental Eng., Dept. of Environmental Eng., Tehran University
3 Member of Water Research Institute, Ministry of Energy, Ph.D. Student of Hydraulic Structures Eng., Tarbiat Modarres University, Tehran.
4 Management Director of CELCO., Ph.D. Student of Environmental Eng., Dept. of Environmental, Tehran University
5 Ph.D. Student of Environmental Eng., Dept. of Environmental, Tehran University
چکیده [English]

Accurate prediction of longitudinal dispersion coefficient (LDC) can be useful for the determination of pollutants concentration distribution in natural rivers. However, the uncertainty associated with the results obtained from forecasting models has a negative effect on pollutant management in water resources. In this research, appropriate models are first developed using ANN and ANFIS techniques to predict the LDC in natural streams. Then, an uncertainty analysis is performed for ANN and ANFIS models based on Monte-Carlo simulation. The input parameters of the models are related to hydraulic variables and stream geometry. Results indicate that ANN is a suitable model for predicting the LDC, but it is also associated with a high level of uncertainty. However, results of uncertainty analysis show that ANFIS model has less uncertainty; i.e. it is the best model for forecasting satisfactorily the LDC in natural streams.

کلیدواژه‌ها [English]

  • Monte-Carlo
  • Longitudinal Dispersion Coefficient
  • Neural Network
  • adaptive neuro-fuzzy inference system
  • Bound Wide Factor
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