Effective flood forecasting at higher lead times through hybrid modelling framework

Artificial neural network has been acknowledged as a promising tool for accurately forecasting the streamflow. However, several constraints limit its application in operational hydrology; the primary one being, the nonavailability (practically not available) of observed streamflow to be used as input to the model. In addition, progressive reduction in forecast accuracy along with an increase in forecast lead time makes the ANN model less amenable for operational flood forecasting. This study proposes a hybrid model (M4) that combines the strength of a physically-based distributed hydrological model and a data-driven model (ANN), which purges the above concerns. The proposed model was tested using two different case studies (ST-I and ST-II), with respective time steps as fifteen minutes and one day. While the first case study demonstrated the application of the model on a real-time streamflow forecasting scenario, the second one represented a continuous streamflow simulation. The performance of the hybrid model was evaluated, and the results showed that the proposed hybrid model (M4) performed reasonably well at higher lead times (NSE = 0.91 for ST-I and 0.77 for ST-II at time step 't + 4'). The proposed model (M4), was further tested for its ability to work with forecasted rainfall (synthetically generated), using the data of ST-I and the model performed well with NSE of 0.95. Though the performance of the models was drawn only up to 't + 4', it was illustrated that the proposed hybrid model could be used to generate forecasted streamflow hydrograph corresponding to a full flood event well in advance. This characteristic of the proposed model enhances its utility in operational flood forecasting.