پیش بینی جریان ماهیانه رودخانه با استفاده از مدلهای داده مبنا

نوع مقاله: یادداشت فنی (5 صفحه)

نویسندگان

1 دانشجوی دکتری/ دانشکده مهندسی عمران. آب و محیط زیست.دانشگاه شهید بهشتی

2 استادیار/ دانشکده مهندسی عمران. آب و محیط زیست. دانشگاه شهید بهشتی

چکیده

در سال‌های اخیر، تکنیک‌های مدل‌سازی داده‌ مبنا کاربردهای فراوانی در مطالعات هیدرولوژی و مهندسی منابع آب یافته‌اند. توسعه مدل‌های برآورد یا پیش‌بینی رواناب رودخانه، یکی از زمینه‌های مطالعاتی است که این تکنیک‌ها در آن کاربرد زیادی دارند. در مطالعه حاضر، چهار تکنیک مدل‌سازی داده ‌مبنا، شامل رگرسیون خطی چندگانه، K نزدیک‌ترین همسایه، شبکه‌های عصبی مصنوعی و سیستم‌های استنتاج عصبی - فازی تطبیقی به‌منظور تشکیل مدل‌های پیش‌بینی رواناب مورد استفاده قرار گرفته و نتایج حاصل بررسی شده است. همچنین تأثیر انتخاب چند سناریوی مختلف در انتخاب متغیرهای ورودی ارزیابی شده است. نتایج به دست آمده حاکی از آن است که استفاده از داده‌های جریان ماه‌های قبل در مجموعه داده‌های مورد استفاده جهت پیش‌بینی می‌تواند سبب بهبود دقت نتایج مدل‌ها شود. به‌علاوه، مقایسه عملکرد کلی تکنیک‌های مدل-سازی، بیانگر برتری نتایج حاصل از به‌کارگیری تکنیک KNN نسبت به سایر تکنیک‌ها می‌باشد. در میان مدل‌های برگزیده تکنیک‌های مختلف نیز، مدل برگزیده KNN برای حالت استفاده از داده‌های جریان با ضریب همبستگی خطی 0.84 بین داده‌های مشاهداتی جریان و پیش‌بینی‌های مدل و مقدار شاخص خطای RMSE برابر 2.64 بهترین عملکرد را به نمایش گذاشت.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Prediction of Monthly Streamflow Using Data-driven Models

نویسندگان [English]

  • A Ahani 1
  • M Shourian 2
1 PhD Student, Department of Civil, Water and Environmental Engineering, Shahid Beheshti University
2 Assistant Professor, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University
چکیده [English]

In recent years, data-driven modeling techniques have gained several applications in hydrology and water resources studies. River runoff estimation and forecasting is one of the research fields in which these techniques have several applications. In the current study, four data-driven modeling techniques, including multiple linear regression, K-nearest neighbors, artificial neural networks and adaptive neuro-fuzzy inference systems have been used to form runoff forecasting models and then their results have been evaluated. Also, effects of using of some different scenarios to select predictor variables have been studied. It is evident from the results that using flow data related to one or two month ago in the predictor variables dataset can improve accuracy of results. In addition, comparison of general performances of the modeling techniques shows superiority of results of KNN models among the studied models. Among selected models of the different techniques, the selected KNN model presented best performance with a linear correlation coefficient equal to 0.84 between observed flow data and predicted values and a RMSE equal to 2.64.

کلیدواژه‌ها [English]

  • Stream flow
  • Monthly prediction
  • Data-Driven Modeling

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