Predicting River Floods Using Discrete Wavelet Transform

The IUP Journal of Soil and Water Sciences, Vol. IV, No. 1, pp. 29-41, February 2012

Posted: 27 Sep 2012

See all articles by Sahay Rajeev Ranjan

Sahay Rajeev Ranjan

Birla Institute of Technology (BIT), Mesra

Anirban Chakraborty

Birla Institute of Technology (BIT), Mesra

Date Written: September 26, 2012

Abstract

The paper demonstrates the efficiency of Wavelet Regression (WR) in estimating floods in rivers when the only data available is historical flow series. Discrete Wavelet Transform (DWT) decomposes the flow series into constituent wavelet components, i.e., approximations and details. A modified flow series is then constructed after removing the most fluctuating components and recombining other wavelet components. The modified flow series forms the input basis for WR implementation. Autoregressive (AR) models, developed for the comparison purpose, were implemented on the original flow series. A case study of developed models was made using monsoon flood data of the Kosi River at Birpur gauge site in the Bihar state of India. Based on various performance indices, it can be concluded that WR models forecast floods with greater accuracy than AR models.

Keywords: autoregression, discrete wavelet transform, floods, Kosi, India

Suggested Citation

Ranjan, Sahay Rajeev and Chakraborty, Anirban, Predicting River Floods Using Discrete Wavelet Transform (September 26, 2012). The IUP Journal of Soil and Water Sciences, Vol. IV, No. 1, pp. 29-41, February 2012, Available at SSRN: https://ssrn.com/abstract=2152301

Sahay Rajeev Ranjan (Contact Author)

Birla Institute of Technology (BIT), Mesra ( email )

Mesra
Ranchi, 835215
India

Anirban Chakraborty

Birla Institute of Technology (BIT), Mesra ( email )

Mesra
Ranchi, 835215
India

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