نشریه محیط زیست طبیعی

تعیین طبقه‌بندی کیفی آب‌ بر اساس حداقل پارامترهای کیفی (مطالعۀ موردی: رودخانۀ کارون)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش‌آموختۀ کارشناسی ارشد مهندسی منابع آب، گروه آبیاری و آبادانی، دانشگاه تهران، کرج، ایران

2 دانشجوی دکتری مهندسی منابع آب، گروه آبیاری و آبادانی، دانشگاه تهران، کرج، ایران

3 استاد گروه آبیاری و آبادانی، دانشگاه تهران، کرج، ایران

4 دانشیار گروه آبیاری و آبادانی، دانشگاه تهران، کرج، ایران

چکیده

رودخانه­ها یکی از مهم­ترین منابع تأمین آب شیرین به­شمار می­روند. محدودیت این منابع ضرورت حفظ کیفیت آن­ها را نشان می­دهد. به‌منظور پایش کیفی منابع آب معمولاً از شاخص­های کیفیت آب استفاده می­شود. هر کدام از این شاخص بر اساس پارامترهای کیفی مشخصی محاسبه می­شوند که فرآیند نمونه­برداری و تعیین مقدار این پارامترها زمان­بر و پرهزینه است، لذا یافتن روشی دقیق که در آن با حداقل پارامترهای کیفی بتوان طبقۀ­ کیفیت آب را تعیین کرد بسیار مفید است. در مقالۀ حاضر از مزایای شبکۀ عصبی احتمالی (PNN) به­عنوان یک طبقه­بندی­کننده برای تعیین کیفیت آب رودخانۀ کارون به­عنوان جایگزینی برای شاخص متداول و پرکاربرد NSFWQI استفاده شد. برای این منظور از آمار کیفی 172 نمونه استفاده شد به این صورت که پارامترهای کیفی و کلاس­های کیفیت آب حاصل از شاخص NSFWQI به ترتیب به عنوان ورودی مدل و خروجی مدل در نظر گرفته شدند. جهت ارزیابی عملکرد مدل PNN، از معیارهای ارزیابی نرخ خطا، مقدار خطا، دقت و ضریب همبستگی اسپیرمن استفاده شد. نتایج نشان داد که PNN تنها با استفاده از سه پارامتر کیفی کدورت، کلیفرم مدفوعی و کل مواد جامد می­تواند با دقت 37/94% و 78/90% به ترتیب در مرحلۀ آموزش و آزمایش، طبقه­ کیفی آب را مشخص کند که بیانگر دقت بالای PNN در تعیین طبقه کیفی آب می­­باشد .

کلیدواژه‌ها

عنوان مقاله [English]

Water Quality Classification Based on Minimum Qualitative Parameter (Case Study: Karun River)

نویسندگان [English]

  • q q 1
  • q q 2
  • Kumars Ebrahimi 3
  • q q 4

1 q

2 q

3 q

4 q

چکیده [English]

Rivers are considered one of the most important resources of providing fresh water. Restrictions faced by such resources underline the necessity of preserving their quality. Water quality indices are usually resorted to qualitatively monitor water resources. Any of such indices is calculated with regard to a series of specific qualitative parameters. The process of sampling and quantification of the aforementioned parameters are, however, time-consuming and costly. Finding a reliable method comprised of minimum qualitative parameters could be, therefore, of great help in classifying water quality. As an alternative to the common NSFQWI, the advantages of the Probabilistic Neural Network (PNN) as a classifier are used in the present study to classify water quality of Karun River. In order to fulfill this objective, the qualitative statistics of 172 samples were used in a way that qualitative parameters and water quality classes derived from NSFWQI are used respectively as the input and output of the model. The assessment criteria of error rate, error value, accuracy and Spearman’s correlation coefficient were used to evaluate the performance of PNN model. The results showed that through making use of merely three parameters of turbidity, fecal coliform and total dissolved solids, PNN model is capable of classifying water quality with the accuracy of 94.365% and 90.7769% at two stages of training and test respectively which in turn indicates considerable accuracy of PNN in determining water quality classification.

کلیدواژه‌ها [English]

  • surface water
  • Water Quality Parameters
  • NSFWQI
  • Water quality classification
  • modeling
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نشریه محیط زیست طبیعی
دوره 70، شماره 3
آذر 1396
صفحه 583-595