A Wavelet-ANN Approach to Investigate the Effect of Seasonal Decomposition of Time Series in Daily River Flow Forecasting

  • سال انتشار: 1394
  • محل انتشار: دهمین کنگره بین المللی مهندسی عمران
  • کد COI اختصاصی: ICCE10_0243
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 1061
دانلود فایل این مقاله

نویسندگان

Mohamad Javad Alizadeh

PhD Student, Faculty of Civil Engineering, K.N.Toosi University of Technology, Tehran, Iran

Mohamad Reza Kavianpour

Associate Professor, Faculty of Civil Engineering, K.N.Toosi University of Technology, Tehran, Iran

Ahmad Tahershamsi

Associate Professor, Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran

Hossein Shahheydari

PhD Student, Faculty of Civil Engineering, K.N.Toosi University of Technology, Tehran, Iran

چکیده

This paper presents the effect of seasonal decomposition of time series in daily flow forecasting. Models are developed for each season separately and a wavelet-neural network approach is applied to predict the flow discharge in Karaj River. Different combinations of the meteorological data (precipitation and temperature) and the flow discharge with different lag times and also different wavelet decomposition levels are used to find the best model performances. Discrete wavelet transform is used to decompose the original time series and the decomposed sub-time series are applied as the new input data for the neural network models. The study demonstrates that wavelet-neural network models can be used to predict the flow discharge successfully. Performances of seasonal models are compared with non-seasonal models. Comparisons show that the use of seasonal models instead of non-seasonal models provides a more accurate prediction of the river flow. Results of this study reveal that the best model for each season includes different input variables

کلیدواژه ها

Wavelet Transform, Neural Network, Flow Discharge, Precipitation, Model Performance

مقالات مرتبط جدید

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.