برآورد میزان شیرابه مراکز دفن زباله با استفاده از شبکه عصبی مصنوعی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 کارشناس ارشد عمران- محیط زیست، عضو هیئت علمی پژوهشکده محیط زیست جهاد دانشگاهی، رشت

2 دانشیار گروه آب و محیط زیست، دانشکده عمران، دانشگاه علم و صنعت ایران، تهران

چکیده

در این مطالعه به‌منظور مدل‌سازی شدت جریان فاضلاب در مراکز دفن زباله از شبکه عصبی مصنوعی استفاده شد. پس از آموزش، شبکه عصبی قادر است براساس داده‌های هواشناسی و مشخصات فاضلاب مرکز دفن، شدت جریان فاضلاب را پیش‌بینی کند. داده‌های ورودی شبکه عصبی شامل پارامترهایی نظیر pH، دما، هدایت الکتریکی فاضلاب مرکز دفن و داده‌های هواشناسی بود. برای ارزیابی و تشریح مدل، مرکز دفن زباله بیروت به‌صورت موردی بررسی شد. از مطالعه انجام شده بر روی مرکز دفن زباله بیروت، داده‌های مورد نیاز برای آموزش و آزمایش شبکه عصبی به‌دست آمد. این مرکز دفن از سال 1997 بهره‌برداری شده و از سال 1998 میزان فاضلاب تولیدی در آن پایش شده است. الگوریتم بهینه از بین سیزده نوع الگوریتم پس انتشار انتخاب شد و برای آموزش شبکه عصبی مورد استفاده قرار گرفت. سپس ساختمان بهینه شبکه عصبی تعیین گردید. در این مطالعه، شبکه عصبی با الگوریتم لونبرگ- مارکوارت که دارای ده نرون در لایه پنهان بود، به‌عنوان شبکه عصبی بهینه انتخاب شد. با توجه به شاخصهای آماری به‌دست آمده (ضریب تعیین= 0/976، میانگین خطای نسبی= 0/089) و داده‌های ورودی در نظر گرفته شده، برآورد شدت جریان فاضلاب در مرکز دفن زباله توسط شبکه عصبی از کارایی مناسبی برخوردار است.
 
 

کلیدواژه‌ها


عنوان مقاله [English]

Modeling Leachate Generation Using Artificial Neural Networks

نویسندگان [English]

  • Mohammad Javad Zoqi 1
  • Mohsen Saeedi 2
1 M.Sc. of Civil- Environmental, Faculty Member of Environmental Research Institute of Jahad Daneshgahi, Rasht
2 Assoc. Prof. of Water and Environmental, Dept. of Civil Eng., Iran University of Science and Tech., Tehran
چکیده [English]

In this study, a neural network model is proposed for modeling leachate flow-rate in a municipal solid waste landfill site. After training, the neural network model predicts leachate generation based on meteorological data and leachate characteristics. Parameters such as pH, temperature, conductivity and meteorological data were used as input data. To validate the proposed method, a case study was carried out based on the data obtained from city ofBeirutlandfill site. While waste disposal at the site started in October 1997, measuring leachate generation rates was not initiated until April 1998. The Levenberg-Marquardt algorithm was selected as the best of thirteen backpropagation algorithms. The optimal neuron number for Levenberg-Marquardt algorithm is 10. The performance of modeling was determined. According to the statistical performance indices (R=0.976, ARE=0.089), the results of the forecast model were in good agreement with measured data.
 
 

کلیدواژه‌ها [English]

  • Artificial Neural Network
  • leachate
  • Flow Rate
  • Meteorological data
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