Wavelets-based non-linear model for real-time daily flow forecasting in Krishna River

R. Maheswaran, Rakesh Khosa

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

In this study, a multi-scale non-linear model based on coupling a discrete wavelet transform (DWT) and the second-order Volterra model, i.e. the wavelet Volterra coupled (WVC) model, is applied for daily inflow forecasting at Krishna Agraharam, Krishna River, India. The relative performance of the WVC model was compared with regular artificial neural networks (ANN), wavelet-artificial neural networks (WA-ANN) models and other baseline models such as auto-regressive moving average with exogenous variables (ARMAX) for lead times of 1-5 days. The models were applied for the forecasting of daily streamflow at Krishna Agraharam Station at Krishna River. The WVC performed very well, especially when compared with the WA-ANN model for lead times of 4 and 5 days. The results indicate that the WVC model is a promising alternative to the other traditional models for short-term flow forecasting.

Original languageEnglish (US)
Pages (from-to)1022-1041
Number of pages20
JournalJournal of Hydroinformatics
Volume15
Issue number3
DOIs
StatePublished - 2013

Keywords

  • Krishna river basin
  • Rainfall-runoff modelling
  • Real-time flood forecasting
  • Volterra model
  • Wavelet-based models

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