بررسی عمکلرد مدل هیبریدی ماشین بردار پشتیبان-الگوریتم گیاهان مصنوعی در تخمین جریان روزانه رودخانه ها(مطالعه موردی:حوضه دز)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری سازه‌های آبی/دانشکده کشاورزی، دانشگاه لرستان.

2 دانشیار /گروه مهندسی آب، دانشکده کشاورزی، دانشگاه لرستان.

3 استادیار/ گروه مهندسی آب، دانشکده کشاورزی، دانشگاه لرستان.

چکیده

برآورد دقیق جریان رودخانه‌های حوضه های آبریز نقش مهمی در مدیریت منابع آب‌ به‌ویژه تصمیمات صحیح در مواقع سیلاب و خشکسالی‌ دارد. در سالهای اخیر جهت برآورد جریان رودخانه‌ها روش های متنوعی در هیدرولوژی معرفی‌شده که مدل‌های هیبریدی هوش مصنوعی از مهم‌ترین آن‌ها است. در این پژوهش یک روش پیشنهادی هیبریدی تحت عنوان ماشین بردار پشتیبان- الگوریتم گیاهان مصنوعی مورد بررسی قرار داده و نتایج آن با مدل ماشین بردار پشتیبان-موجک مقایسه گردید. به منظور برآورد دبی رودخانه های حوضه آبریز دز، از آمار آبدهی روزانه ایستگاههای هیدرومتری واقع در بالادست سد طی دوره آماری(1397-1387) استفاده شد. معیارهای ضریب تبیین، ریشه میانگین مربعات خطا، میانگین قدر مطلق خطا و ضریب نش ساتکلیف برای ارزیابی و مقایسه مدلها مورد استفاده قرار گرفت. نتایج نشان داد ساختارهای ترکیبی نتایج قابل قبولی در مدلسازی دبی رودخانه ارائه می نمایند. مدل هیبریدی پیشنهادی ماشین بردار پشتیبان-گیاهان مصنوعی با ضریب همبستگی (985/0-933/0R=)، ریشه میانگین مربعات خطا ( m3/s088/0-008/0RMSE=)، میانگین قدرمطلق خطا ( m3/s040/0-004/0MAE= ) و ضریب نش ساتکلیف (995/0-951/0NS=) عملکرد بهتری در تخمین جریان داشته و می‏تواند در زمینه پیش بینی دبی روزانه رودخانه ها مفید باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Aplication of the Hybrid Model of Support Vector Machine-Algorithm Artificial Flora in Estimating the Daily Flow of Rivers (Case study: Dez basin)

نویسندگان [English]

  • Reza Dehghani 1
  • Hassan Torabi poudeh 2
  • Hojatolah Younesi 3
  • Babak SHahinejad 3
1 Ph.D. Student of Water Structure, Faculty of Agriculture, Lorestan University, Iran.
2 Associate Professor, Department of Water Engineering, Lorestan University
3 Assistant Professor, Department of Water Engineering, Lorestan University, Iran.
چکیده [English]

River flow prediction is one of the key issues in the management and planning of water resources, in particular the adoption of proper decisions in the event of floods and droughts. To predict the flow rate of rivers, various approaches have been introduced in hydrology, the most important of which are the intelligent models. In this study, a hybrid artificial flora- support vector machine model was applied to estimate the discharge of Dez Basin based on the daily discharge statistics provided by the hydrometric stations located at the upstream of the dam during the statistical period (2008-2018) and its performance was compared with the wavelet-support vector machine model. The correlation coefficients, root mean square error, and mean absolute error was used for evaluation and a comparison of the performance of models. The results showed that the hybrid structures presented acceptable outcomes in the modeling of river discharge. A comparison of models also showed that the hybrid model correlation coefficient (R= 0.933-0.985), root-mean-square error (RMSE = 0.008-0.088 m3/s), mean absolute error (MAE=0.008-0.088 m3/s) and the Nash–Sutcliffe coefficient (NS=0.951-0.995) has had better performance in estimating the flow. The results of the study of the charts disclosed that the suggested hybrid model has a suitable performance in estimating the minimum and maximum points and has fewer error in all selected stations.

کلیدواژه‌ها [English]

  • Artificial Flora Alghorithm
  • Prediction
  • Dez Basin
  • Support Vector Machine
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