مدل‌سازی اکسیژن محلول با استفاده از روش یادگیری عمیق و روش‌های پیش‌پردازنده

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی آب، دانشکده مهندسی عمران، دانشگاه تبریز، تبریز، ایران.

چکیده

آلودگی آب یک مشکل بزرگ جهانی است که به ارزیابی مداوم و تجدیدنظر در سیاست منابع آبی در همه سطوح احتیاج دارد. اکسیژن محلول (DO) یکی از مهم­ترین شاخص­های کیفیت آب است. در مطالعه حاضر، پارامتر کیفی اکسیژن محلول در آب با استفاده از روش هوشمند حافظه طولانی کوتاه‌مدت (LSTM) بر پایه روش­های پیش‌پردازنده تبدیل موجک گسسته (DWT) و روش تجزیه مد تجربی کامل (CEEMD) در دو حالت زمانی و مکانی در پنج ایستگاه متوالی بر روی رودخانه ساواناه مورد بررسی قرار گرفت. نتایج حاصل از تحلیل مدل­ها قابلیت و کارایی بالای روش به‌کاررفته را در تخمین میزان اکسیژن محلول در آب به خوبی نشان داد. از طرفی دیگر روش­های پیش‌پردازنده باعث بهبود نتایج شدند. هم‌چنین در بررسی­های انجام‌شده مشاهده شد که نتایج حاصل از تجزیه براساس تبدیل موجک در مدل‌سازی مکانی، به میزان دو درصد و هم‌چنین تجزیه مد تجربی در مدل‌سازی زمانی، به میزان 15 درصد میزان خطای RMSE را کاهش داد. بهترین حالت ارزیابی برای داده­های آزمون با استفاده از تجزیه مد تجربی در حالت مدل‌سازی زمانی مربوط به یک روز قبل با مقادیر 977/0=DC، 988/0=R و 017/0=RMSE به‌دست آمد. هم‌چنین در مدل‌سازی مکانی جهت تخمین اکسیژن محلول در ایستگاه سوم نیز مشخص شد نتایج حاصل از ورودی­های پارامتر اکسیژن محلول در یک روز قبل ایستگاه دوم و دو روز قبل ایستگاه اول بهترین نتیجه را دارا می­باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Dissolved Oxygen Modeling Using Deep Learning and Pre-Processor Methods

نویسندگان [English]

  • kiyoumars roushangar
  • Sina Davoudi
Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran.
چکیده [English]

Water pollution is a major global problem that requires constant evaluation and revision of water resources policy at all levels. Dissolved oxygen (DO) is one of the most important indicators of water quality. In the present study, the water quality parameter of dissolved oxygen using intelligent Long Short-Term Memory (LSTM) method based on discrete wavelet transform (DWT) and Complementary Ensemble Empirical Mode Decomposition (CEEMD) pre-processor methods in both temporal and spatial modes. It was investigated in five consecutive stations on the Savannah River. The results of analysis of models showed the ability and high efficiency of the method used in estimating the amount of dissolved oxygen in water. On the other hand, pre-processor methods improved the results. It was also observed in the investigations that the results of analysis based on wavelet transformation in spatial modeling reduced the RMSE error by two percent and also the empirical mode decomposition in temporal modeling by 15 percent. The best evaluation for test data was obtained using the empirical mode decomposition in temporal modeling corresponding to the previous day with values ​​of DC=0.977, R=0.988 and RMSE=0.017. Also, in the spatial modeling to estimate dissolved oxygen in the third station, it was found that the results obtained from the inputs of the dissolved oxygen parameter one day before the second station and two days before the first station have the best results.

کلیدواژه‌ها [English]

  • Dissolved Oxygen
  • Empirical Mode Decomposition
  • Long Short-Term Memory
  • Wavelet Transform

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مدیریت آب و آبیاری
دوره 12، شماره 4
بهمن 1401
صفحه 983-890

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