Development and application of a hybrid artificial neural network model for simulating future stream flows in catchments with limited in situ observed data

Abstract

ABSTRACT The need to develop new and computationally efficient artificial intelligence models that accurately simulate river flows in data-scarce regions, considering not only current but also projected future climate change conditions is vital. In this study, a hybrid artificial neural network (ANN) model that combines HEC-HMS and the feed-forward neural network (FFNN) was developed in the Python programming language and applied to simulate future stream flows in the River Mayanja catchment in Central Uganda. The study results suggest that the performance of the validated hybrid HEC-HMS-ANN model during calibration and validation (NSE and R2 > 0.99) was more superior to the corresponding performance obtained using individual HEC-HMS (NSE and R2 > 0.50), MIKE HYDRO (NSE and R2 > 0.42), and ANN models (NSE and R2 > 0.56). Using the developed hybrid ANN model, future average daily stream flows are projected to increase by up to 17.3% [2.2–39.5%] and 18.5% [0.8–42.7%] considering the SSP2-4.5 and SSP5-8.5 future climate change scenarios. The study demonstrates that well-trained hybrid ANN models could provide more computationally efficient models for the simulation of future stream flow and for undertaking water resource assessments in catchments with limited in situ observed data.