مقایسه مدلهای شبکه عصبی موجک، ماشین بردار پشتیبان و برنامه ریزی بیان ژن در تخمین میزان اکسیژن محلول در اب رودخانه ها

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار گروه مهندسی آب، دانشگاه لرستان

2 دانشجو

چکیده

اکسیژن محلول در آب از موثرترین پارامترها در تعیین کیفیت آب رودخانه ها بوده و کنترل آن در رودخانه ها از مهم ترین عوامل توسعه منابع آب هر منطقه است. به همین دلیل در این پژوهش عملکرد مدلهای شبکه عصبی موجک، ماشین بردار پشتیبان و برنامه ریزی بیان ژن را جهت تخمین اکسیژن محلول در آب رودخانه کامبرلند واقع در ایالت تنسی مورد بررسی قرار گرفت. برای این منظور سری زمانی ماهانه شاخص DO رودخانه کامبرلند در طی یک دوره 10 ساله (2006-2016) با استفاده از پارامترهای دبی جریان و دما شبیه سازی شد. معیارهای ضریب همبستگی، ریشه میانگین مربعات خطا و میانگین قدر مطلق خطا برای ارزیابی و عملکرد مدلها مورد استفاده قرار گرفت. نتایج نشان داد ساختارهای ترکیبی در هر سه مدل عملکرد بهتری نسبت به سایر ساختارها ارائه می دهد. همچنین نتایج حاصل از معیارهای ارزیابی نشان داد از بین مدلهای بکار رفته، مدل شبکه عصبی موجک با بیشترین ضریب همبستگی (960/0)، کمترین جذر میانگین مربعات خطا (668/0) و نیز کمترین میانگین قدرمطلق خطا (519/0) را در مرحله صحت سنجی دارا می باشد. در مجموع نتایج نشان داد به لحاظ توانایی بالای شبکه عصبی موجک و حذف نویزهای سری های زمانی در تخمین پارامترهای کیفی آب رودخانه، این مدل می‌تواند، راهکاری مناسب و سریع در مدیریت کیفیت منابع آب و اطمینان از نتایج پایش کیفی و کاهش هزینه های آن مطرح شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Comparison of wavelet neural network models, support vector machine and gene expression programming in estimating the amount of oxygen dissolved in rivers

نویسندگان [English]

  • Babak Shahinejad 1
  • Reza Dehgani 2
1 Assistant Professor   Department of Water Engineering
چکیده [English]

Water-soluble oxygen is one of the most effective parameters in determining the water quality of rivers and its control in the rivers is one of the most important factors for the development of water resources in each region. For this reason, in this study, the performance of wavelet neural network models, support vector machines and gene expression scheduling was investigated to estimate the oxygen dissolved in the water of the Cumberland River in Tennessee. For this purpose, the monthly time series DO of the Cumberland River index were simulated using flow and temperature flow parameters over a 10-year period (2006-2016). The correlation coefficient, root mean square error and mean absolute error value were used for evaluation and performance of the models. The results showed that hybrid structures in all three models offer better performance than other structures. Also, the results of the evaluation criteria showed that the wavelet neural network model with the highest correlation coefficient (0.960), the lowest root mean square error (0.668), and the lowest mean error of error (0.519) in the verification stage were among the applied models. In total, the results showed that in terms of the high ability of wavelet neural network and the elimination of time series noise in the estimation of river water quality parameters, this model could be a suitable and fast way to manage the quality of water resources and ensure the results of quality monitoring and cost reduction.

کلیدواژه‌ها [English]

  • Dissolved oxygen
  • Gene expression programming
  • Wavelet Neural Network
  • Water Quality
  • Support Vector Machine
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