مقایسه عملکرد روش‌های یادگیری عمیق و یادگیری ماشین در پیشبینی میزان اکسیژن محلول

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استاد، گروه مهندسی آب، دانشکده مهندسی عمران، دانشگاه تبریز، تبریز، ایران

2 دانشجوی کارشناسی ارشد آب و سازه‌های هیدرولیکی، گروه مهندسی آب، دانشکده مهندسی عمران، دانشگاه تبریز، تبریز، ایران

چکیده

پیش‌بینی کیفیت آب نقش مهمی در پایش زیست‌-محیطی، پایداری اکوسیستم و آبزی‌پروری ایفا می‌کند. روش‌های پیش‌بینی سنتی نمی‌توانند غیر خطی و غیر ثابت بودن کیفیت آب را به خوبی نشان دهند. در مطالعه حاضر پارامتر کیفی اکسیژن محلول در آب با استفاده از روش‌های هوشمند ماشین بردار پشتیبان (SVM)، رگرسیون فرآیند گاوسی (GPR) و روش حافظه طولانی کوتاه-مدت (LSTM) بر روی سه ایستگاه متوالی بر روی رودخانه ساواناه واقع در ایالات متحده آمریکا مدل‌سازی شد. بدین منظور شش پارامتر هیدرولیکی و هیدرولوژیکی جریان شامل دمای آب، کدورت، دبی، میانگین سرعت جریان، pH و رسانایی ویژه در مدت هفت سال (2015-2021) به صورت روزانه به عنوان پارامترهای ورودی، جهت مدل‌سازی اکسیژن محلول به کار گرفته شدند. نتایج نشان‌دهنده برتری کامل روش یادگیری عمیق بر روش‌های یادگیری ماشین بود. با توجه به نتایج بدست آمده روش حافظه طولانی کوتاه-مدت برای مدل آخر که شامل تمامی پارامترها بود در ایستگاه سوم با دارا بودن ضریب همبستگی و ضریب تبیین و جذر میانگین مربعات خطا به ترتیب 981/0R= و 956/0DC= و 034/0RMSE= برای داده‌های آزمون از عملکرد بهتری برخوردار بود. در نهایت با انجام تحلیل حساسیت، با حذف پارامتر دمای آب، مشخص گردید معیارهای ارزیابی DC، به میزان 14% کاهش و RMSE، به میزان 100% افزایش داشت. بنابراین دمای آب به عنوان تأثیرگذارترین پارامتر در پیش‌بینی اکسیژن محلول در آب معرفی شد. 

کلیدواژه‌ها

عنوان مقاله [English]

Comparing the Performance of Deep Learning and Machine Learning Methods in Predicting Dissolved Oxygen Content

نویسندگان [English]

  • kiyoumars roushangar 1
  • Sina Davoudi 2
1 Professor, Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran
2 M.Sc. of Water and Hydraulic Structures, Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran
چکیده [English]

Water quality forecasting plays an important role in environmental monitoring, ecosystem sustainability and aquaculture. Traditional forecasting methods cannot show the non-linearity and instability of water quality well. In the present study, the water quality parameter of dissolved oxygen was modeled using intelligent Support Vector Machine (SVM), Gaussian Process Regression (GPR) and Long Short-Term Memory (LSTM) methods on three consecutive stations on Savanah River located in USA. For this purpose, six different flow hydraulic and hydrological parameters including water temperature, turbidity, discharge, mean water velocity, pH and specific conductivity were used daily for seven years (2021-2015) as input parameters to model dissolved oxygen. The results showed the complete superiority of the deep learning method over the machine learning methods. According to the results, the long short-term memory method for the last model, which included all parameters, in the third station with correlation coefficient, coefficient of determination and root mean square error, respectively R = 0.981, DC = 0.956 and RMSE = 0.034 for test data performed better. Finally, by performing sensitivity analysis, by removing the water temperature parameter, it was found that DC evaluation criteria decreased by 14% and RMSE increased by 100%. Therefore, water temperature was introduced as the most influential parameter in predicting dissolved oxygen in water.

کلیدواژه‌ها [English]

  • Dissolved Oxygen parameter
  • Long Short-Term Memory
  • Water Quality
  • Support Vector Machine
  • Gaussian Process Regression

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تحقیقات آب و خاک ایران
دوره 53، شماره 8
آبان 1401
صفحه 1885-1900

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