Flow Forecasting in Multiple Sections of a River System

A river system includes the combination of flows occurring simultaneously in the main river and its contributing tributaries. Any change in the flow condition of the river system is caused due to changes in flow of the main river and/or contributing tributaries. An accurate flow forecasting at multiple sections of a river system is worthy for issuing early warning to the imminent floods and in regulating the reservoir outflows. The application of multiple-inputs and multiple-outputs (MIMO) model is an effective way for simultaneous flow forecasting as it provides interrelation among multiple input and multiple output variables simultaneously. In the present study an Artificial Neural Networks (ANN) based MIMO model has been developed for Barak river system in Assam, India using Partially Recurrent Neural Network (PRNN) and Nonlinear Autoregressive with Exogenous Inputs (NARX) approaches. Performance of the model using both NARX and PRNN provide an efficacy with coefficient of efficiency (CE) > 0.87 and Mean Absolute Percentage Error (MAPE) < 7.66% at 12 hour lead time forecasting. This indicates satisfactory model performances for simultaneous flow forecasting at multiple sections of a river system however the results obtained by MIMO using NARX (MIMO-NARX) perform better than MIMO using PRNN (MIMO-PRNN) in terms of statistical performance criterion.