نشریه محیط زیست طبیعی

ارزیابی عملکرد روش‌های شبکۀ عصبی مصنوعی و زمین‌آمار در شبیه‌سازی پارامترهای کیفی آب‌های زیرزمینی (مطالعۀ موردی: شهر کوهپایه، استان اصفهان)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار گروه محیط زیست، دانشکدۀ منابع طبیعی، دانشگاه تهران

2 کارشناس ارشد محیط زیست، دانشکدۀ منابع طبیعی، دانشگاه تهران

3 کارشناس ارشد بیابان‌زدایی، دانشکدۀ منابع طبیعی، دانشگاه تهران

4 دانشجوی دکتری آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه تهران، ایران

چکیده

آب‌های زیرزمینی، مهم‌ترین منبع آب مصرفی در مناطق خشک و نیمه‌خشک در بخش‌های مختلف از قبیل کشاورزی، صنعت و شرب است. مدیریت این منابع آبی نسبت به آب‌های سطحی مشکل‌تر و پرهزینه‌تر است. به همین دلیل باید به دنبال روش‌هایی معقول و مقرون به صرفه برای مشخص‌کردن وضعیت این آب‌ها بود. در این مطالعه از روش‌های زمین‌آماری کریجینگ و کوکریجینگ و همچنین شبکۀ عصبی پروسپترون چند‌لایه به‌منظور برآورد پارامترهای کیفی -SO42، TDS، Ca و TH استفاده شد تا ضمن مقایسۀ این روش‌ها با هم بهترین روش نیز در این زمینه انتخاب شود. بدین منظور از داده‌های 50 حلقه چاه دشت کوهپایۀ استان اصفهان استفاده شد. به‌منظور ارزیابی عملکرد روش‌های مذکور در شبیه‌سازی پارامترهای مطالعه‌شده از خطای جذر میانگین مربعات (RMSE) و ضریب همبستگی استفاده شد. نتایج حاصل از مقایسۀ سه روش نشان داد که در مورد همۀ پارامترها، شبکۀ عصبی پروسپترون چند‌لایه با RMSE کمتر و ضریب همبستگی بالاتر دقت بهتری نسبت به روش‌های کریجینگ و کوکریجینگ دارد و بین دو روش زمین‌آماری کریجینگ و کوکریجینگ نیز، روش کوکریجینگ با RMSE کمتر و ضریب همبستگی بالاتر عملکرد بهتری نسبت به روش کریجینگ در برآورد همۀ پارامترهای مطالعه‌شده از خود نشان داد.

کلیدواژه‌ها

عنوان مقاله [English]

The evaluation of the functionality of artificial neural network and geostatistical methods in simulation of quality parameters of groundwater (Case study: Koohpayeh, Isfahan)

نویسندگان [English]

  • Amir Hossein Hamidian 1
  • Majid Atashgahi 2
  • Mohammadreza Hajihashemijazi 3
  • Abolhasan Fathabadi 4

1 Assistant Professor, Department of Environment, Faculty of Natural Resources, University of Tehran, I.R. Iran

2 MSc. Graduated, Department of Environment, Faculty of Natural Resources, University of Tehran, I.R. Iran

3 MSc. Graduated, Department of Rehabilitation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, I.R. Iran

4 PhD. Candidate, Department of Rehabilitation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, I.R. Iran

چکیده [English]

Groundwater resources are the most important sources of water consumption (agriculture, industry and drinking water) in arid and semi-arid regions. The management of these water resources is more expensive and difficult than surface waters; therefore more complicated and economic methods are needed for determination of their quality and quantity. In this study geostatistical methods of kringing and cokriging and multilayer perceptron (MLP) artificial neural network model were used to estimate the quality parameters of SO42-, TDS, Ca and TH. The methods were compared in order to understand which one is the best method for this estimation. Data obtained from 50 wells located in Koohpayeh plain in Isfahan province was used for this study. For estimation of the functionality of these methods in simulation of the parameters, RMSE and correlation coefficient were used. The results showed that for all of the studied parameters, MLP with a lower RMSE and higher correlation coefficient showed the highest precision followed by cokriging. Kriging showed to have the lowest precision in stimulation of the quality parameters.

کلیدواژه‌ها [English]

  • MLP Neural Network
  • Kriging
  • Cokriging
  • RMSE
  • Koohpayeh
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نشریه محیط زیست طبیعی
دوره 67، شماره 3 - شماره پیاپی 3
آبان 1393
صفحه 279-288