Application of Neural Networks for Hydrologic Process Understanding at a Midwestern Watershed

The Shell Creek Watershed (SCW) is a rural watershed in Nebraska with history of chronic flooding. Beginning 2005, variety conservation practices have been employed the watershed. Those since credited attenuating flood severity and improving water quality SCW. This study investigated impacts 13 different controlling factors on flooding at SCW by using an artificial neural network (ANN)-based rainfall-runoff model. Additionally, frequency analysis drought were conducted. Special emphasis was placed understanding how trends change light to determine whether any relation exists between peak attenuation, as strategic plan implemented provides unique opportunity examine potential ANN model developed this showed satisfactory discharge–prediction performance, Kling–Gupta Efficiency (KGE) value 0.57. It found that no individual variable used significantly better predictor SCW, therefore all variables inputs, which resulted performance. Furthermore, it observed after planning magnitude anomalous flows increased, while annual decreased. However, more comprehensive assessment necessary identify relative basin.