Modeling Stream Flow with Prediction Uncertainty by Using SWAT Hydrologic and RBNN Models for an Agricultural Watershed in India

Abstract

Simulation of hydrological processes at the watershed outlet is essential for proper planning and implementation of appropriate soil conservation measures in the Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the parametric uncertainty involved in simulation of stream flow using the soil and water assessment tool (SWAT) watershed scale model and radial basis neural network (RBNN), an artificial neural network model. Both the models were calibrated/trained and validated and quantification of the uncertainty in model output was assessed using “sequential uncertainty fitting algorithm” and the Bootstrap technique. The RBNN model performed better than SWAT with R2 and NSE values of 0.92 and 0.92 during training, and 0.71 and 0.70 during validation period, respectively. The values of P-factor related to each model shows that the percentage of observed stream flow values bracketed by the 95PPU in the RBNN model as 91 % is higher than the P-factor in SWAT as 87 %. In other words the RBNN model estimates the stream flow values more accurately and with less uncertainty. It could be stated that the RBNN model based on simple input could be used for estimation of monthly stream flow, missing data, and testing the accuracy and performance of other models.