ارزیابی رویکرد پیش‌پردازش میانگین متحرک در تدقیق پیش‌بینی جریان ورودی به سدها توسط مدل رگرسیون بردار پشتیبان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 فارغ‌التحصیل کارشناسی ارشد گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

2 دانشیار، گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

3 استاد، گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

چکیده

پیش‌بینی دقیق هیدرولوژیکی یک ابزار کلیدی در برنامه­ریزی‌های منابع آب است. از این‌رو در این مقاله با بهره­گیری از مدل­های رگرسیون بردار پشتیبان (SVR)، رگرسیون چند متغیره­ی خطی  (MLR)و خود همبسته‌ی میانگین متحرک (ARMA)، جریان ورودی به سدهای بختیاری و رودبار لرستان پیش­بینی شده است. به منظور پیش­پردازش داده­های ورودی مدل­ها از رویکرد میانگین متحرک استفاده شد. برای ارزیابی کارایی مدل­ها از معیارهای ارزیابی نش ـ ساتکلیف (NSE)، جذر میانگین مربعات خطا (RMSE)، ضریب همبستگی (R) و دیاگرام تیلور استفاده شد. نتایج نشان داد که استفاده از روش پیش­پردازش میانگین متحرک باعث بهبود عملکرد مدل­های مورد استفاده شده است. بطوری که مقادیر NSE مربوط به مدل SVR با پیش­پردازش میانگین متحرک در پیش­بینی جریان ورودی به سدهای بختیاری و رودبار لرستان نسبت به مدل SVR بدون پیش­پردازش به ترتیب ۴/۱۳ و ۶/۶ درصد بهبود داشته است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluation of Moving Average Pre-processing Approach to Improve the Efficiency of Support Vector Regression Model for Inflow Prediction

نویسندگان [English]

  • Mahdi Abbasi 1
  • Shahab Araghinejad 2
  • Kumars Ebrahimi 3
1 MSc in Water Resources Engineering, Department of Irrigation & Reclamation Engineering, University of Tehran, Karaj, Iran
2 Associate Professor, Department of Irrigation and Reclamation Engineering, University of Tehran, Karaj, Iran
3 Professor, Department of Irrigation and Reclamation Engineering, University of Tehran, Karaj, Iran
چکیده [English]

Accurate hydrologicalforecasting is a main tool for the water resources planning. In this paper, the inflow rates to Bakhtiari and Rudbar Dams in Lorestan province – IRAN, were forecasted using support vector regression (SVR), Multiple Linear Regression (MLR) and Autoregressive Moving Average (ARMA) models. In order to pre-process the input data for the above mentioned models, the moving average approach was used. Furthermore, Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), correlation coefficient (R) and Taylor diagram were used to evaluate the efficiency of the models. The results showed that the moving average pre-processing approach improved the performance of the above mentioned models dramatically. For instance, the values of Nash-Sutcliff correspond to SVR hybrid model in forecasting inflow rate to Bakhtiari and Rudbar-Lorestan dams with moving average pre-processing were improved by 13.4% and 6.6%, respectively, as compared to those in the SVR model without pre-processing.

کلیدواژه‌ها [English]

  • Forecasting Time Series
  • Support Vector Regression
  • Moving Average
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