Runoff prediction based on GM-BP model calibration against Markov chain
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Abstract:
In order to further improve the accuracy of mid and long term runoff prediction,taking the runoff data series of 1959-2014 in Lanxi Hydrological Station for case study,the grey model and BP neural network model are applied to predict the runoff depth respectively,and the prediction results are corrected with the state probability derived from the Markov chain,and,furthermore,the corrected results of both models are coupled by least square method.The statistical descriptions of the corrected and combined prediction results show 12.72% in average relative error,11.70 in mean square deviation of better than the grey model and BP neural network model,and 90.91% of the prediction results,satisfying with the threshold of relative error. less than 20%.The shortcomings of the single model may be effectively overcome by applying the coupled model,and the prediction precision be further improved by adopting the corrected results based on the Markov chain.With more efficient fitting and more accurate prediction,the corrected and combined model suggested in this study is of practical value in prediction of the mid and long term runoff.