کاربرد مدل‌های بهینۀ عصبی فازی در تخمین شاخص کیفی آب رودخانه‌ی کارون

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، گروه مهندسی آب، دانشکده مهندسی عمران و نقشه برداری، دانشگاه تحصیلات تکمیلی کرمان

2 دانشجوی کارشناشس ارشد مهندسی مدیریت منابع آب دانشگاه تحصیلات تکمیلی کرمان

چکیده

مدیریت کیفیت آب مستلزم اتخاذ تصمیمات صحیح مدیریتی است و لازمه این امر پیش بینی و تخمین کیفیت آب در بدنه‌های آبی می‌باشد. استفاده از روش های هوش مصنوعی از جمله مدل‌های کارا در پیش بینی متغیرها و شاخص های کیفیت آب می‌باشد. در این تحقیق، در ابتدا با استفاده از سیزده متغیر ورودی کیفیت آب شامل اکسیژن محلول، اکسیژن موردنیاز شیمیایی، اکسیژن موردنیاز بیولوژیکی، هدایت الکتریکی، نیترات، نیتریت، فسفات، کدورت، شاخص اسیدیته، کلسیم، منیزیم، سدیم و دما مقادیر شاخص کیفی (WQI) ماهانه بر اساس دستور‌العمل موسسه بهداشت ملی (NSF) برای نه ایستگاه آب‌سنجی رودخانۀ کارون تخمین زده شده است. سپس، از روش‌های آنالیز حساسیت آزمون گاما (GT)، آنالیز مؤلفه‌های اصلی (PCA) و انتخاب پیشرو متغیرها (FS) به منظور دست‌یابی به انتخاب بهینه متغیرهای ورودی به مدل هوشمند سیستم استنتاجی عصبی-فازی تطبیقی (ANFIS) استقاده گردید. در نهایت، ضرایب ثابت توابع عضویت موجود در ساختار مدل ANFIS با استفاده از چهار الگوریتم‌های بهینه‌ساز کلونی مورچگان (ACO)، وراثتی (GA) و ازدحام ذرات (PSO) محاسبه گردیدند. نتایج شاخص‌های آماری نشان داد که مدل ترکیبی GT-ANFIS-PSO با داشتن مقادیر ضریب همبستگی، میانگین خطای مطلق و جذر میانگین مربعات خطا به ترتیب برابر با0/952، 1/68 و 3/05 در مرحلۀ آزمایش در مقایسه با سایر مدل‌های ترکیبی دارای عملکرد بهتری می‌باشد. همچنین، مقادیر شاخص کیفی آب در بازه20 تا 58/4 قرار گرفتند که بیانگر کیفیت نسبتاً بد تا خوب آب رودخانه کارون می‌باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Application of Optimized Neuro-Fuzzy Models for Estimation of Water Quality Index in Karun River

نویسندگان [English]

  • Mohammad Najafzadeh 1
  • Muhammad Lottfi-Dashbalagh 2
1 Department of Water Engineering, Faculty of Civil and Survey Engineering, Graduate University of Advanced Technology, Kerman
2 Water Engineering Department, Faculty of Civil and Surveying Engineering
چکیده [English]

Management of water quality is inextricably bound up with making good management decisions and this typical management is at the mercy of predicting the water quality index (WQI). The use of board range of artificial intelligence models for analyzing surface water quality is one of the most efficient techniques to predict water quality parameters and WQI. In the current research, at the first, datasets accumulated from nine hydrometry stations, located in Karun River, were included those of 13 water quality parameters (i.e., dissolved oxygen, chemical oxygen demand, biochemical oxygen demand, electrical conductivity, nitrate, nitrite, phosphate, turbidity, pH, calcium, magnesium, sodium, and water temperature) which was used to estimate WQI. So, to obtain an optimal selection of ANFIS model-feeding-input variables, gamma test (GT), forward selection (FS), and principal component analysis (PCA) evaluations were applied. Ultimately, constant coefficients of membership function used in the ANFIS model were computed by using evolutionary techniques including a genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) for training the structure of the ANFIS model. Results of statistical assessments indicated that the GT-ANFIS-PSO model with a correlation coefficient of 0.952, mean absolute error of 1.68, and root mean square error of 3.05 had a satisfying performance for prediction of WQI compared with other optimized ANFIS models. Moreover, values of WQI ranged from 30 to 58.4 which were indicative of being relatively poor to the good water quality of Karun River. 

کلیدواژه‌ها [English]

  • Water Quality Index
  • Adaptive neuro-fuzzy inference system
  • Sensitivity analysis
  • Heuristic Algorithms
  • Karun River
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