Uncertainty Quantification of Granular Computing-neural Network Model for Prediction of Pollutant Longitudinal Dispersion Coefficient in Aquatic Streams

Abstract 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 ( D x ), a key parameter with large temporal and spatial fluctuates that characterizes pollution transport. The large uncertainty in estimation of D x 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 D x and its uncertainty for different flow-geometric conditions with high spatiotemporal variability. Uncertainty analysis of D x 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 D x 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 D x data (i.e., 100%) is the best model to compute D x in streams. Given considerable inherent uncertainty reported in other D x models, the D x GrC-ANN model is suggested as a proper tool for further studies of pollutant mixing in turbulent flow systems such as streams.