In this study, MIKE NAM, artificial neural networks (ANNs), and a hybridization of ANNs and Particle Swarm Optimization (ANN-PSO) are utilized to predict the Dak Nong runoff. ANNs are trained by the back-propagation (BP) procedure [1] which is based on the gradient descent algorithm and an incorporating algorithm of PSO and BP [2]. Moreover, to improve the performance of ANNs, a common method of time series analysis, so-called partial autocorrelation function (PACF), is collaboratively used. The experimental results are conducted on a dataset collected in the Dak Nong basin for the duration of 1981–2007. The experiments demonstrate that PACF significantly impacts the performance of ANNs. Although ANN-PSO is not superior to ANNs trained by BP (ANN-BP) in this study, ANN-PSO outperforms ANN-BP in terms of capturing the peaks of the Dak Nong runoff. In addition, both ANN-BP and ANN-PSO outperform MIKE NAM.