Discharge of pollution loads into natural water systems remains a global challenge that threatens water/food supply as well as endangers ecosystem services. Natural rehabilitation of the polluted streams is mainly influenced by the rate of longitudinal dispersion (Dx), a key parameter with large temporal and spatial fluctuates that characterizes pollution transport. The large uncertainty in estimation of Dx in streams limits evaluation of water quality in natural streams and design of water quality enhancement strategies. This study develops a sophisticated model coupled with granular computing and neural network models (GrC-ANN) to provide robust prediction of Dx and its uncertainty for different flow-geometric conditions with high spatiotemporal variability. Uncertainty analysis of Dx GrC-ANN model was based on the alteration of training data fed to tune the model. Modified bootstrap method was employed to generate different training patterns through resampling from a 503 global database of tracer experiments in streams. Comparison between the Dx values estimated by GrC-ANN to those determined from tracer measurements show the appropriateness and robustness of the proposed method in determining the rate of longitudinal dispersion. GrC-ANN model with the narrowest bandwidth of estimated uncertainty (bandwidth-factor =0.56) that brackets the most percentage of true Dx data (i.e., 100%) is the best model to compute Dx in streams. Given considerable inherent uncertainty reported in other Dx models, the Dx GrC-ANN model is suggested as a proper tool for further studies of pollutant mixing in turbulent flow systems such as streams.