**6. Conclusion**

This chapter presents an investigation of the performance of the Gene Expression Programming (GEP) models of suspended sediment load of the Mississippi River, the USA. The study employs the Mississippi River data spanning 26 years involving both flows and suspended sediment load, of which the first 25 years of the data is used for training and the remaining for the prediction of one year into the future. This investigation concurs with the past findings that the performance of sediment rating curve, an empirical technique used widely in practice, can lead to gross errors. This alone underlines the value of other modelling techniques capable of producing reliable results with less than 1% of errors.

The chapter promotes a pluralist culture of modelling and although presents the GEP model as the focus, it also presents the application of other techniques to model the same data. The other models comprise: artificial neural networks, multi-linear regression analysis and deterministic chaos. The chapter outlines the modelling strategy underlying each of these techniques and the results show in spite of their differences they produce similar results inflicting less 1% of errors. The lowest errors are associated with the artificial neural networks for this set of data but each of these techniques should be considered as reliable. The volume of sediment load is an important management parameter and the error associated with each model was estimated for each model. The results show that the traditional SRC model suffers from gross errors by as much as 50% but the other tested models perform well, among which the error in the performance of ANN is the lowest. ANN is noted for its good performance but with some drawback that these models are not transferrable, like the GEP models. It is noted that the implementation of ANN requires an ANN-platform for further modelling and deterministic chaos models require considerable expertise.
