Application of an improved neural network to flood forecasting of the Lower Yellow River
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.
flood forcasting Non-linear perturbation model Artificial Neural Networks Linear Perturbation Model
Bo Pang Yuan Liang
College of Water Science, Beijing Normal University Key Laboratory of Water and Sediment Science, Ministry of Education Beijing 100875, China
国际会议
杭州
英文
423-426
2011-10-28（万方平台首次上网日期，不代表论文的发表时间）