Artificial Neural Network Approach for Streamflow Forecasting in India Using ENSO and EQUINOO

It is well recognized that hydrologic time series, like rainfall, streamflow etc. are modulated by various large-scale atmospheric circulation patterns, like El Niño Southern Oscillation (ENSO) etc., throughout the globe by climatic teleconnection. Indian summer monsoon rainfall (ISMR) is proved to be significantly influenced by ENSO. Recently, it is established that relationship between ISMR and ENSO is modulated by the influence of atmospheric circulation pattern over Indian Ocean region, like, Equatorial Indian Ocean Oscillation (EQUINOO). Thus, for Indian subcontinent, hydrologic time series are significantly influenced by ENSO along with EQUINOO. Though influence of these large-scale atmospheric circulations on rainfall is investigated, their influence on streamflow is yet to be investigated. In this paper, information of ENSO from tropical Pacific Ocean and EQUINOO from tropical Indian Ocean are used in terms of their corresponding indices for streamflow forecasting. To model the nonlinear, time-varying and dynamic relationship between streamflow and such large-scale atmospheric circulation information, Artificial Neural Network (ANN) methodology has been opted for the present study. Efficient optimization of ANN architecture is obtained by using evolutionary optimizer based on genetic algorithm. This study concludes that use of such large-scale atmospheric circulation information potentially improves the performance of streamflow prediction which, in turn, helps in better management of water resources.