نوع مقاله : پژوهشی

نویسندگان

1 استادیار گروه مهندسی آب

2 دانشجوی دکترای سازه های آبی ، دانشگاه لرستان

3 استادیار گروه مهندسی آب، دانشکده‌ی کشاورزی، دانشگاه لرستان

چکیده

امروزه مدل­های هیبریدی هوش مصنوعی به عنوان یک روش مناسب برای شبیه­سازی پدیده­های هیدرولوژیکی از جمله برآورد کمی جریان رودخانه‌ها مطرح است. بدین منظور جهت برآورد میزان آبدهی رودخانه‌ها رویکردهای متنوعی در هیدرولوژی وجود دارد که مدل‌های هوش مصنوعی از مهم‌ترین آن‌ها می‌باشد.  بنابراین در این پژوهش عملکرد مدل­های رگرسیون بردار پشتیبان_ موجک، رگرسیون بردار پشتیبان_گرگ خاکستری و رگرسیون بردار پشتیبان_خفاش جهت شبیه­سازی دبی رودخانه‌ کشکان واقع در استان لرستان طی دوره­ی آماری 1399-1389 در مقیاس زمانی روزانه­ی مورد تجزیه و تحلیل قرار گرفت. معیارهای ضریب همبستگی، ریشه­ی میانگین مربعات خطا و میانگین قدر مطلق خطا و بایاس برای ارزیابی و عملکرد مدل‌ها انتخاب شد. نتایج نشان داد الگوهای ترکیبی نتایج قابل قبولی در شبیه­سازی دبی رودخانه دارند. مقایسه­ی مدل‌ها نیز نشان داد مدل رگرسیون بردار پشتیبان-موجک در مرحله­ی صحت­سنجی مقادیر 960/0R2=، 045/0RMSE=، 024/0MAE =، 968/0NS= و001/0BIAS= در پیش‌بینی جریان روزانه­ی رودخانه از خود نشان داده است. در مجموع نتایج نشان داد استفاده از مدل هیبریدی رگرسیون بردار پشتیبان-موجک می‏تواند در زمینه­ی پیش‌بینی دبی روزانه مفید باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Evaluation of Meta-heuristics Hybrid Models for the River Flow Simulation (Case Study: The River Kashkan, Lorestan, Iran

نویسندگان [English]

  • hojatolah younesi 1
  • ahmad godarzi 2
  • Masoud Shakarami 3

1 Assistant Professor of Water Engineering Department

2 PhD student Water structures

3 Assistant Professor, Department of Water Engineering, Faculty of Agriculture, Lorestan University

چکیده [English]

Today, hybrid models of artificial intelligence are considered as a suitable method for simulating hydrological phenomena, including quantitative estimation of river flow. For this purpose, there are various approaches in hydrology to estimate the flow rate of rivers, of which artificial intelligence models are the most important. Therefore, in this study, the performance of support vector-wavelet regression, backup vector-gray wolf regression and bat-support vector regression models to simulate the flow of Kashkan river located in Lorestan province during the statistical period of 2010-2011 in the daily time scale were analyzed. The criteria of correlation coefficient, root mean square error and mean absolute value of error and bias were selected for evaluation and performance of the models. The results showed that the hybrid models have acceptable results in simulating the river discharge. Comparison of models also showed that the support-wavelet vector regression model in the validation stage showed values ​​of R2 = 0.960, RMSE = 0.045, MAE = 0.024, NS = 0.968 and BIAS = 0.001 in predicting daily river flow. . Overall, the results showed that the use of hybrid support-wavelet regression model can be useful in predicting daily discharge.

کلیدواژه‌ها [English]

  • River flow
  • Simulation
  • Hybrid model
  • Kashkan-Lorestan Province
 
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