Prediction of bedload transport rate using a block combined network structure

Modularity as a system of separate and independent sub-tasks is the appropriate way to improve the performance of artificial neural network (ANN) models in hydrological processes. Using this approach, a block combined neural network (BCNN) structure incorporated with genetic algorithm (GA) and an additional decision block is suggested in this study. The optimum topology of embedded networks in each block was detected using a vector-based method subjected to different internal characteristics. This model was then applied on 879 bedload datasets, considering velocity, discharge, mean grain size, slope, and depth as model inputs over streams in Idaho, USA. The correct classification rate of predicted bedload using BCNN (89.77%) showed superior performance accuracy compared to other ANNs, and to empirical models. Results of computed error metrics and confusion matrixes also demonstrated outstanding progress in BCNN relative to other models. We show that BCNN as a new method with an appropriate accuracy level could effectively be adopted for bedload prediction purposes.

Automated summary

Modularity as a system of separate and independent sub-tasks is proposed to improve the performance of artificial neural network models in hydrological processes. The block combined neural network (BCNN) structure incorporated with genetic algorithm and an additional decision block is suggested in this study. Results show that BCNN outperforms other ANNs and empirical models in bedload prediction.