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Application of an Improved Neural Network to Flood Forecasting of the Lower Yellow River

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2 Author(s)
Bo Pang ; Key Lab. of Water & Sediment Sci., Beijing Normal Univ., Beijing, China ; Yuan Liang

Considering seasonal feature of the flood events, a nonlinear perturbation model based on Artificial Neural Network is developed. The model structure is similar to that of the Linear Perturbation Model. The deference is that ANN, instead of linear response function, was used to simulate the unknown relationship between the input perturbing terms and the output perturbing terms. The reach from Huayuankou to Sunkou, located in the lower yellow river, is selected to test flood forecasting with this model. The proposed model was also compared with the LPM model and ANN model. It was found that the NLPM-ANN model was significantly more efficient than the original linear perturbation model. The results demonstrate that the relationship between the perturbations is high nonlinearity though subtracting the seasonal means and ANN is capable to simulate the relationship. The results also indicate that considering the seasonal information can improve the model efficiency. Subtracting the seasonal means, which adopted in the LPM, is also a feasible way to reduce the system complexity and improve the model efficiency of ANN models.

Published in:

Computational Intelligence and Design (ISCID), 2011 Fourth International Symposium on  (Volume:2 )

Date of Conference:

28-30 Oct. 2011