برآورد ضریب پخشیدگی طولی رودخانه با استفاده از انواع روش‌های داده‌کاوی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار دانشکدة کشاورزی و منابع طبیعی دانشگاه اردکان

2 دانشجوی دکتری علوم و مهندسی آب دانشگاه صنعتی اصفهان

چکیده

در مدل‌سازی و تعیین دقیق وضعیت آلودگی رودخانه‌ها محاسبة دقیق ضریب پراکندگی طولی آلودگی بسیار اهمیت دارد. برای محاسبة این ضریب، معادلات گوناگون با استفاده از روش‌های تجربی، تحلیلی، و ریاضی ارائه شده است. با وجود این، روش‌های تحلیلی و ریاضی به علت پیچیدگی محاسبات و روش‌های تجربی به سبب خطای زیاد تا کنون مورد توجه قرار نگرفته‌اند. این تحقیق به بررسی روش‏ها و معادلات تجربی مختلف برای تعیین ضریب پراکندگی طولی آلودگی در رودخانه‌های طبیعی و ارزیابی دقت این روش‌ها در مقایسه با داده‌های اندازه‌گیری‌شدة واقعی ‌پرداخت و روشی دقیق‌تر در این زمینه، با بهره جستن از روش‌های داده‏کاوی، همچون برنامه‌ریزی ژنتیک، شبکة عصبی، و شبکة عصبی‌ـ فازی ارائه شد. با به‌کارگیری مدل نروفازی، معیارهای ریشة مربعات خطا و ضریب تبیین به ترتیب 21/72 و 87/0 و ضریب جرم باقی‌مانده 103/0 و کارآیی مدل 75/0 به دست آمد. به این ترتیب، روش نروفازی جهت پیش‌بینی ضریب پخشیدگی طولی رودخانه پیشنهاد می‏شود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Prediction of Longitudinal Dispersion Coefficient in Natural Streams using Soft Computing Techniques

نویسندگان [English]

  • Somayyeh Soltani-Gerdefaramarzi 1
  • Ruhollah Taghizadeh-Mehrjerdi 1
  • Mohsen Ghasemi 2
1 Assistant Professor, Faculty of Agriculture and Natural Resources, Ardakan University
2 PhD Candidate, Water Engineering, Isfahan University of Technology
چکیده [English]

To accurately estimate the longitudinal dispersion coefficient is important and indispensable in river modeling. Many theoretical as well as empirical formulations have been proposed to determine the longitudinal dispersion coefficient, but these have not been put into consideration because of their great error, and as well the complexity of the phenomenon. The main aim followed in the present paper is to investigate the method as well as equations developed for dispersion coefficient estimation and assessment of the accuracy of these methods in comparison with real data and developing an accurate methodology for dispersion coefficient determination making use of such soft computing techniques as, neural, genetic programming and Neuron-Fuzzy Inference System.ANFIS approach ended up with the excellent results of: R2 = 0.87, RMSE = 72.21, CRM = 0.103 and EF=0.75 as compared with the existing predictors of dispersion coefficient. In total ANFIS approach is hereby proposed as a most acceptable technique for estimating the longitudinal dispersion coefficient.

کلیدواژه‌ها [English]

  • Soft computing techniques
  • Pollution
  • river
  • longitudinal dispersion coefficient

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