کاربرد آنتروپی شانون برای انتخاب ورودی‌های بهینه درپیش‌بینی جریان رودخانه بااستفاده از مدل-های هوشمند(مطالعه موردی: صوفی چای)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی منابع آب، دانشکده کشاورزی، دانشگاه تبریز.

2 دانشیار بخش مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز.

چکیده

       پیش‌بینی جریان رودخانه‌ یکی از موارد مهم در مدیریت منابع آب‌های سطحی، بهره برداری از مخازن سدها، کنترل سیلاب و خشکسالی می­باشد. انتخاب ورودی‌های مناسب برای افزایش دقت وکارایی مدل‌های هوشمند از اهمیت به­سزایی برخوردار است. در این تحقیق به ارزیابی عملکرد آنتروپی شانون درتعیین ورودی‌های مناسب و کارایی مدل‌های، شبکه عصبی مصنوعی و شبکه بیزین، در پیش‌بینی جریان ماهانه­ی رودخانه­ی صوفی چای در ایستگاه تازه­کند، در طی بازه­ی زمانی(1352-1391) پرداخته شده است. آنتروپی شانون، به‌عنوان روشی برای بیان اطلاعات یا عدم قطعیت یک پدیده مورداستفاده قرار می­گیرد. هم­چنین به‌عنوان شاخصی برای کمی کردن میزان عدم آگاهی و دانش نسبت به مشخصات یک سامانه، استفاده می­شود. داده­های جریان ماهانه با تأخیرهای مختلف به‌عنوان ورودی به آنتروپی شانون در نظر گرفته­شد. نتایج آنتروپی نشان داد که جریان رودخانه با سه تأخیر زمانی، عملکرد بهتری نشان می­دهد. سپس نتایج حاصل از پیش‌بینی، با دو مدل شبکه عصبی مصنوعی و شبکه بیزین مقایسه گردید. عملکرد این دو مدل با شاخص­های آماری ضریب تبیین (R2)، جذر میانگین مربعات خطا(RMSE) و شاخص پراکندگی(SI) ارزیابی شد. نتایج نشان داد که مدل شبکه عصبی مصنوعی با ضریب تبیین بیشتر و جذر میانگین مربعات خطا کمتر و شاخص پراکندگی کمتر در مقایسه با شبکه بیزین، از کارایی بالاتری برخورداراست و ازاین رو آنتروپی شانون در تعیین ترکیب بهینه­ی ورودی­ها به مدل­های هوشمند مناسب می­باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Application of Shannon Entropy for Selecting the Optimum input Variables in River Flow Simulation using Intelligent Models (Case Study: SofyChay)

نویسندگان [English]

  • fateme Akhoni Pourhosseini 1
  • Mohammad Ali Ghorbani 2
1 Ms.c student of Water Resources Engineering, University of Tabriz, Iran
2 Associate Professor, University of Tabriz, Iran.
چکیده [English]

       Accurate   prediction of the  river flow is  an  important  element  in  the management  of  surface  water  resources, dam  reservoir  operation, flood control and drought. Selecting appropriate inputs for intelligent models is vital to increase the accuracy and efficiency of the models. Since river flow prediction is of great importance in water resources, researchers have been exploring different approaches over the past several decades.  Various methods have been devised to predict the flow of the river over the past years. In general, we can classify conceptual models and data-driven methods.  Over the past four decades, time series models have been widely used in river flow prediction (Dawson et al., 2008). Intelligent systems are used to predict nonlinear phenomena. The Bayesian Network and the Artificial Neural Network are among these methods. Ahmadi et al. (2014) studied the  comparison of performance of support vector machine and network methods in forecasting daily flow of the Barandozachay River. The results showed that, both methods are close to each other and are suitable for river flow simulation. But in mid-range forecasting and the minimum backup car model, it's much better than the business network model. Shannon entropy theory was first developed by Shannon and then widely used in various scientific issues.
The purpose of this study is to use the Shannon Entropy Theory to find the best combination of input variables for artificial neural network and Bayesian network models to predict the flow. Therefore, for this purpose, the Sufi River of the studied area was selected.

کلیدواژه‌ها [English]

  • Artificial neural network
  • Bayesian Network
  • Entropy
  • river flow
  • Sofychay River

مراجع

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علوم و مهندسی آبیاری
دوره 41، شماره 2
تیر 1397
صفحه 183-195

فایل ها

سابقه مقاله

  • تاریخ دریافت: 11 مرداد 1395
  • تاریخ بازنگری: 26 دی 1395
  • تاریخ پذیرش: 11 اسفند 1395
  • تاریخ انتشار: 01 تیر 1397

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