Wavelet regression models for predicting flood stages in rivers: a case study in Eastern India

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 Kosi River in Bihar State of India. During monsoon (June to Oct), the Kosi carries large flow and makes the entire North Bihar 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 Kosi River, with the highest accuracy of 97.41%, the minimum root mean square error of 7.9cm and the maximum coefficient of correlation of 0.952.