In this study linear genetic programming (LGP), which is a variant of Genetic Programming, and two versions of Neural Networks (NNs) are used in predicting time-series of daily flow rates at a station on Schuylkill River at Berne, PA, USA. Daily flow rate at present is being predicted based on different time-series scenarios. For this purpose, various LGP and NN models are calibrated with training sets and validated by testing sets. Additionally, the robustness of the proposed LGP and NN models are evaluated by application data, which are used neither in training nor at testing stage. The results showed that both techniques predicted the flow rate data in quite good agreement with the observed ones, and the predictions of LGP and NN are challenging. The performance of LGP, which was moderately better than NN, is very promising and hence supports the use of LGP in predicting of river flow data.

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Bridge pier scouring is a significant problem for the safety of bridges. Extensive laboratory and field studies have been conducted examining the effect of relevant variables. This note presents an alternative to the conventional regression-based equations (HEC-18 and regression equation developed by authors), in the form of artificial neural networks (ANNs) and genetic programming (GP). 398 data sets of field measurements were collected from published literature and used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in training. The performance of GP was found more effective when compared to regression equations and ANNs in predicting the scour depth of bridge piers.

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