Optimizing parameters for two conceptual hydrological models using a genetic Algorithm: A case study in the Dau Tieng River Watershed, Vietnam

In recent years, many conceptual hydrological models have been constructed to calculate rainfall runoff for river watersheds, two of which, the Tank Model and the NAM Model, have been widely used in many Asian countries to forecast flooding and manage water resources because of their simplicity. However, obtaining good results can be time-consuming and costly because multiple model parameters must be calibrated. This requirement has led to an increased need for automated calibration. In this study, the two hydrological models had a genetic algorithm (GA) incorporated to model the rainfall runoff process and optimize model parameters. Calibration data were obtained at hydrological gauges of the river system upstream of the Dau Tieng River watershed, located along the upper Saigon River in Southeastern Vietnam. The GA optimization in this study concurrently adjusted eighteen of the Tank Model parameters and ten NAM Model parameters to improve modeling efficiency. The study concluded that both models showed good correlation between simulated and observed flows, with increased accuracy and convenience. The Tank Model produced better simulation results through error indicators such as root mean square error, efficiency, and relative error.