Discrete wavelet-Ann approach in time series flow forecasting-a case study of Brahmaputra river

Abstract

This paper deals with the prediction of hydrologic behavior of the runoff for the one of the largest discharge carrier International River, Brahmaputra, located in Assam (India) at the Pandu station, by using daily time unit. The flow regime dominated by high data non-stationary and seasonal irregularity due to Himalayan climate fallout. The influence of data preprocessing through wavelet transforms has been investigated. For this, the main time series of flow data were decomposed to multi resolution time series using discrete wavelet transformations. Then these decomposed data were used as input to Artificial Neural Network (ANN) for multiple lead time flow forecasting. Various types of wavelets were used to evaluate the optimal performance of models developed. The forecasting accuracy of the models has been tested for multiple lead time upto 4 days using different decomposition levels. The performance of the proposed hybrid model has been evaluated based on the performance indices such as root mean square error (RMSE), coefficient of efficiency (CE) and mean relative error (MRE).The results shows the better forecasting accuracy by the proposed combined hybrid model over the single ANN model in hydrological time series forecasting. © 2012 CAFET-INNOVA TECHNICAL SOCIETY.