Open AccessWavelet regression models for predicting flood stages in rivers: a case study in E astern I ndia
Author(s) -
Sahay R.R.,
Sehgal V.
Publication year - 2013
Publication title -
journal of flood risk management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.049
H-Index - 36
ISSN - 1753-318X
DOI - 10.1111/j.1753-318x.2012.01163.x
Subject(s) - series (stratigraphy) , mathematics , autoregressive model , statistics , type (biology) , wavelet , mean squared error , discrete wavelet transform , correlation coefficient , algorithm , wavelet transform , computer science , artificial intelligence , geology , biology , ecology , paleontology
Combining discrete wavelet transform ( DWT ) and autoregression ( AR ), two types of wavelet regression ( WR ) models were developed for forecasting 1‐day‐ahead river stages. In the first type of WR models, AR was applied on the DWT ‐obtained subtime series while in the second type, AR was applied on the modified time series which was formed by recombining effective subtime series and ignoring the ‘noise’ subtime series. Depending upon different input combinations, five models in each type of WR models were developed. The efficiency of developed models was tested in forecasting monsoon stages of K osi R iver in B ihar S tate of I ndia. During monsoon ( J une to O ct), the K osi carries large flow and makes the entire N orth B ihar unsafe for habitation or cultivation. When compared, WR models predicted river stages with greater accuracy than AR and artificial neural network ( ANN ) models, developed for the purpose. Between the two types of WR models, the first type gave slightly better results than the second type. The best performing WR model, with five previous days’ subtime series as inputs, predicted stages of the K osi R iver, with the highest accuracy of 97.41%, the minimum root mean square error of 7.9 cm and the maximum coefficient of correlation of 0.952.