Neuroevolution methodologies applied to sediment forecasting

摘要

Sediment forecasting represents a significant modelling challenge. This is due to the combined effects of suspended sediment transfer and throughput being source limited and subject to hysteresis effects. Recent approaches to modelling and foiecasting have involved the use of neural networks. Despite yielding good results, this method has its own set of limitations, for example lack of guidance in parameter setting and the potential to overtrain This paper reports on the application of a neuroevolutionary toolbox, lavaSANE This toolbox is applied to two catchments in Puerto Rico that have been previously studied by Kisi (2005), who used a range of different methods including a neuro-fuzzy approach and neural networks to model suspended sediment in these catchments These experiments are replicated using JavaSANE and compared to the results reported in Kisi (2005). These results show that JavaSANE produces estimates that are better or comparable to those of Kisi (2005).