Openly accessible

Improving daily peak flow forecasts using hybrid fourier-series autoregressive integrated moving average and recurrent artificial neural network models

Banihabib, Mohammad Ebrahim, Bandari, Reihaneh and Valipour, Mohammad 2020, Improving daily peak flow forecasts using hybrid fourier-series autoregressive integrated moving average and recurrent artificial neural network models, AI, vol. 1, no. 2, pp. 263-275, doi: 10.3390/ai1020017.

Attached Files
Name Description MIMEType Size Downloads

Title Improving daily peak flow forecasts using hybrid fourier-series autoregressive integrated moving average and recurrent artificial neural network models
Author(s) Banihabib, Mohammad Ebrahim
Bandari, Reihaneh
Valipour, Mohammad
Journal name AI
Volume number 1
Issue number 2
Start page 263
End page 275
Total pages 13
Publisher MDPI AG
Place of publication Basel, Switzerland
Publication date 2020
ISSN 2673-2688
Summary In multi-purpose reservoirs, to achieve optimal operation, sophisticated models are required to forecast reservoir inflow in both short- and long-horizon times with an acceptable accuracy, particularly for peak flows. In this study, an auto-regressive hybrid model is proposed for long-horizon forecasting of daily reservoir inflow. The model is examined for a one-year horizon forecasting of high-oscillated daily flow time series. First, a Fourier-Series Filtered Autoregressive Integrated Moving Average (FSF-ARIMA) model is applied to forecast linear behavior of daily flow time series. Second, a Recurrent Artificial Neural Network (RANN) model is utilized to forecast FSF-ARIMA model’s residuals. The hybrid model follows the detail of observed flow time variation and forecasted peak flow more accurately than previous models. The proposed model enhances the ability to forecast reservoir inflow, especially in peak flows, compared to previous linear and nonlinear auto-regressive models. The hybrid model has a potential to decrease maximum and average forecasting error by 81% and 80%, respectively. The results of this investigation are useful for stakeholders and water resources managers to schedule optimum operation of multi-purpose reservoirs in controlling floods and generating hydropower.
Language eng
DOI 10.3390/ai1020017
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30153539

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 7 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Wed, 14 Jul 2021, 13:24:10 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.