ارزیابی عملکرد روش های شبیه سازی-بهینه سازی ANN-WOA و ANN-BWO در پیش بینی رواناب روزانه (مطالعه موردی: ایستگاه جلوگیر در حوضه آبریز کرخه)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مدیریت ساخت و آب، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

2 دانشیار، گروه مدیریت ساخت و آب، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

3 دانشیار، گروه مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه ارومیه، ایران.

4 استاد، گروه مهندسی آب، قطب علمی هیدروانفورماتیک، دانشکده مهندسی عمران، دانشگاه تبریز، شرکت مهندسین مشاور فرازآب ، دفتر برنامه ریزی و مدیریت طرح ها، تبریز، ایران.

چکیده

مدل سازی بارش-رواناب روزانه به دلیل تعدد عوامل مؤثر آن، یکی از پیچیدگی‌های علم هیدرولوژی است. ترکیب‌های مختلفی از عوامل بارش-رواناب، طی دوره آماری 10 ساله (1390-1400) به‍عنوان ورودی برای تخمین رواناب در مقیاس زمانی روزانه مورد ارزیابی قرار گرفت. از 80 درصد داده‌ها  به عنوان آموزش (2920 نمونه) و 20 درصد داده‌ها به عنوان آزمون (730 نمونه) استفاده گردید. عوامل ورودی شامل بارش (P) و دبی (Q) که برای بارش تا شش تأخیر  و برای دبی تا چهار تأخیر  استفاده گردید. از ضریب همبستگی پیرسون برای ارتباط بین متغیرهای ورودی و خروجی استفاده گردید. بر این اساس ترکیب مدل شماره  یک شامل صرفا بارش و دبی، دارای بیشترین همبستگی (805/0) و ترکیب سیزدهم (بارش و بارش از یک تا شش روز تأخیر  و دبی از یک تا چهار روز تأخیر ) دارای کمترین همبستگی (109/0) بوده است. به ‍منظور مدل‍سازی از مدل‌های شبکه عصبی مصنوعی- الگوریتم بهینه ‍سازی نهنگ (ANN-WOA)، شبکه عصبی مصنوعی-الگوریتم بهینه ‍سازی عنکبوت سیاه (ANN-BWO) و مدل شبکه عصبی-موجک کلاه مکزیکی (WANN) استفاده گردید. همچنین برای ارزیابی مدل از شاخص‌های ریشه میانگین مربعات خطا (RMSE)، میانگین قدرمطلق خطا (MAE)، ضریب بهره وری نش- ساتکلیف (NSE) و ضریب نا اریبی (PBIAS) استفاده گردید. یافته‌های پژوهش نشان می‌دهد که کلیه مدل‌های فوق عملکرد بسیار خوبی در پیش ‍بینی فرایند بارش-رواناب از خود نشان دادند. در این بین مدل ANN-BWO دارای بهترین عملکرد در پیش ‍بینی بوده است. همچنین مدل‌های  ANN-WOAو  WANN و  ANN-BWOبه ‍ترتیب 4/32 و 6/27 و 14/22 درصد دقت مدل منفرد شبکه عصبی را بهبود بخشیدند.    

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Performance evaluation of ANN-WOA and ANN-BWO simulation-optimization methods in predicting daily runoff (case study: Jelogir station in Karkheh watershed)

نویسندگان [English]

  • Edris Merufinia 1
  • Ahmad Sharafati 2
  • Hirad Abghari 3
  • Yousef Hassanzadeh 4
1 Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 Department of Range and Watershed Management, Urmia University, Urmia, Iran.
4 Department of Water Engineering, Center of Excellence, Faculty of Civil Engineering, University of Tabriz -Farazab Consulting Engineers, PMO, Tabriz, Iran .
چکیده [English]

The key to social and economic development is water, an essential natural resource. Worldwide, many areas are experiencing water supply and demand mismatches or are under extreme stress due to water shortages. Water resources have been mismanaged or limited due to an increase in demand and limitations in available water supply (Banadkooki et al., 2019). Rainfall and runoff are considered to be the main components of the hydrological cycle. In order to capture the dynamic relationship between rainfall and runoff, engineers need to develop an accurate model (Tikhamarine et al., 2022). Rainfall-runoff modeling is one of the methods of estimating runoff and a suitable tool for studying hydrological processes, evaluating water resources and watershed management (Abrahart and See, 2000). But the complexity and non-linear nature of the rainfall-runoff process and the unknown effect of the factors on each other and finally on the outflow of the basin make modeling more difficult (Moriasi et al., 2007). Therefore, it is necessary to use methods that, in addition to dynamism, have the ability to develop, have a conceptual and user-friendly structure (Shi et al., 2012). The role and importance of the mentioned process in water resources studies has caused this process to be noticed by experts (Guven, 2009). Therefore, several methods such as artificial neural networks, fuzzy and neuro-fuzzy systems, wavelet analysis, genetic algorithm, genetic programming and stochastic differential equations have been developed to model the rainfall-runoff process (Yaseen et al., 2016; Zhang et al., 2019). The development of rainfall-runoff models using different AI models has been conducted several times in the past two decades, but these models still have several shortcomings. These drawbacks are usually related to overfitting, difficulty in initializing the internal parameters related to these models and proposing the proper input-output architecture of the model (Ahmed et al., 2019).

کلیدواژه‌ها [English]

  • Black widow algorithm
  • Rainfall-Runoff prediction
  • correlation coificeint
  • Wavelet

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علوم و مهندسی آبیاری
دوره 47، شماره 3
آبان 1403
صفحه 19-36

فایل ها

سابقه مقاله

  • تاریخ دریافت: 19 مهر 1401
  • تاریخ بازنگری: 25 شهریور 1402
  • تاریخ پذیرش: 26 شهریور 1402
  • تاریخ انتشار: 01 آبان 1403

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