ارزیابی کارآیی الگوریتم های آموزش شبکه‌های عصبی مصنوعی برای برآورد پارامترهای کیفی آب دشت قروه-دهگلان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 فارغ التحصیل کارشناسی ارشد، گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه زابل، زابل، ایران

2 دانشیار، گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه زابل، زابل، ایران

چکیده

چکیده
مقدمه: شبکه عصبی مصنوعی (ANN) یک ابزار قدرتمند داده محور است که قادراست روابط خطی و غیرخطی حاکم بر سیستم های مختلف را فراگیرد. اما هنوز تعیین الگوریتم دارای بهترین عملکرد ازنظر سرعت و دقت همگرایی برای یک مسئله خاص، چالش مهم پیش روی کاربران شبکه های عصبی مصنوعی است.
روش­: در این پژوهش، توانایی فرآیندهای پرکاربرد طی چند سال اخیر در بحث شبیه سازی و تخمین پارامترهای غیرخطی کیفیت آب بررسی و اثربخش ترین آن ها تعیین گردید. برای این منظور، 42 مدل از ترکیب توپولوژی شبکه عصبی مصنوعی (تک لایه یا چندلایه)  و فرآیندهای آموزش بررسی گردید. پارامترهای کیفی مشاهده شده در محل 107 چاه  در گستره آبخوان دشت قروه-دهگلان از سال 75 الی 92 جهت آموزش و داده های سال های 93 تا 95 جهت آزمون هر مدل استفاده شد.
یافته­ ها: نتایج نشان داد که شبکه های عصبی مصنوعی دارای یک لایه پنهان که از تعداد بهینه نورون بهره می برند، قادرند رفتار آبخوان را با دقت مطلوب و در مدت‌زمان کمتر شبیه سازی نمایند. همچنین، افزایش تعداد لایه های میانی همگام با افزایش دقت پاسخ، نه‌تنها تعداد سلول های بهینه شبکه بلکه مدت‌زمان تحلیل مساله را افزایش می دهد. به هرحال، شبکه های عصبی مصنوعی مبتنی بر روش Broyden-Fletcher-Goldfarb (BFG) از بیشترین کارآیی در شبیه‌سازی رفتار آبخوان برخوردار است، گرچه کارآیی روش Levenberg Marquart (LM) بسیار نزدیک به آن است. کارآیی بیشتر BFG نسبت به LM می‌تواند به دلیل برخورداری از میانگین خطا و انحراف معیار کمتر (به ترتیب برابر 46/3 و 09/3) آن باشد.
نتیجه ­گیری: شبکه های عصبی مصنوعی با یک لایه پنهان و تعداد بهینه نورون می توانند رفتار آبخوان را با دقت مطلوبی شبیه سازی کنند و نسبت به شبکه های عصبی مصنوعی متشکل از چندین لایه پنهان کارایی بیشتری دارند.

کلیدواژه‌ها

عنوان مقاله [English]

The Efficiency Evaluation of Artificial Neural Network Training Algorithms for Estimation of Water Quality Parameters of Qorveh-Dehgolan Plain

نویسندگان [English]

  • Seyed Ashkan Seyed Ebrahimi 1
  • Abuzar Nekuie 1
  • Mahmoud Reza Mollaeinia 2
1 M.Sc. graduate, Civil Engineering Department, Faculty of Technology and Engineering, University of Zabol, Zabol, Iran
2 Associate Professor, Civil Engineering Department, Faculty of Technology and Engineering, University of Zabol, Zabol, Iran
چکیده [English]

Abstract
Introduction: An artificial neural network (ANN) is a powerful data-driven tool capable of learning the linear and nonlinear relationships governing different systems. However, determining the best-performing algorithm in terms of convergence speed and accuracy for a particular problem is still a fundamental challenge for users of artificial neural networks.
Methods: We investigated the most effective algorithm among widely used processes to simulate and estimate nonlinear water quality parameters. For this purpose, we constructed 42 models combining artificial neural network topology (single or multilayer) and training processes. The quality parameters’ data acquired at 107 wells throughout the aquifer of Qorveh-Dehgolan plain were used for training (data from 1996 to 2013) and to test (data from 2014 to 2016) each model.
Findings: The results showed that artificial neural networks with a hidden layer that benefits from the optimal number of neurons could simulate the aquifer behavior with high accuracy and in less time. Also, increasing the number of hidden layers while increasing the response accuracy increases the number of optimal network neurons and the duration of the problem analysis. Finally, artificial neural networks based on the Broyden-Fletcher-Goldfarb (BFG) method had the highest efficiency in simulating aquifer behavior, although the performance of the Levenberg Marquart (LM) method is very close to BFG. BFG is more efficient than LM due to its lower Mean Square Error and Standard Deviation (3.46 and 3.09, respectively).

کلیدواژه‌ها [English]

  • Simulation
  • Artificial Neural Network
  • Network Training Algorithms
  • Qualitative Parameter Estimation
  • Qorveh-Dehgolan Plain

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سابقه مقاله

  • تاریخ دریافت: 09 اسفند 1398
  • تاریخ بازنگری: 02 خرداد 1400
  • تاریخ پذیرش: 10 بهمن 1402

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