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

Content Provider	IEEE Xplore Digital Library
Author	Bo Pang Yuan Liang
Copyright Year	2011
Abstract	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.
Starting Page	43
Ending Page	46
File Size	372374
Page Count	4
File Format	PDF
ISBN	9781457710858
DOI	10.1109/ISCID.2011.112
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Non-linear perturbation model Linear Perturbation Model Artificial Neural Networks Discharges Artificial neural networks Predictive models Data models Rivers Floods Forecasting flood forcasting
Content Type	Text
Resource Type	Article

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