MATEC Web of Conferences
Volume 25, 20152015 International Conference on Energy, Materials and Manufacturing Engineering (EMME 2015)
|Number of page(s)||7|
|Published online||06 October 2015|
Application of Grey Prediction and BP Neural Network in Hydrologic Prediction
1 Pearl River Hydraulic Scientific Research Institute of Pearl River Water Resources Commission, Guangzhou, Guangdong, China
2 Pearl River Basin Monitoring Center of Soil and Water Conservation of Pearl River Water Resources Commission, Guangzhou, Guangdong, China
* Corresponding author: firstname.lastname@example.org
Based on the data of annual runoff at Boluo Hydrologic Station in the Dongjiang River of Guangdong Province from 1954 to 2010, this paper establishes a prediction model of an annual runoff through three methods, that is, the regression analysis, the grey theory and the neural network. The prediction model which is established by the regression analysis method has passed F-statistical test (P=0.05), but the relative error of predicted value at 30% of the data point is over ±30%, and its prediction precision is general. The precision of the residual prediction model of an annual runoff GM (1, 1) that established based on the grey theory is obviously better than that of the former one; the relative error of predicted value at only 10% of the data point is over ±30%; the Nash statistical coefficient (NS) of predicted value and measured value is 0.627, and the correlation coefficient (R2) is 0.774. For the prediction model of BP neural network, the relative error at about 5% of the data point is over ±30%, and NS=0.66, R2=0.853. In general, the precision and the reliability of the neural network prediction model of an annual runoff are the best.
Key words: hydrologic prediction / regression analysis / grey prediction / neural network
© Owned by the authors, published by EDP Sciences, 2015
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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