The scour depth that develops around bridge piers is known to be related to flow intensity, particle size of bed sediment, and pier dimensions. Earlier approaches to this issue have mainly relied on empirical formulas. Even numerical simulations have not been so successful due to problems associated with interactions between water flow and streambed morphology. This necessitates the application of an artificial intelligence (AI)-type approach to understanding the effects of local scour around bridge piers. Although previous studies reported that AI-based models are better predictors, they do not predict field-scale local scour well. Motivated by this, the present study reports on the use of data quality assessment with an artificial neural network (ANN) model for predicting field-scale scour depth around bridge piers. Both univariate and multivariate methods were applied and the predicted results are compared. For the multivariate method, the Euclidean distance method and Mahalanobis distance method were used and the predicted results are compared. The ANN model was first trained and validated using laboratory data and the model was applied to data obtained in laboratory experiments. The model was then applied to field data. Quantitative descriptions are given on how much the data quality assessment improves predictions based on the use of the ANN model.