Rainfall-runoff Forecasting Based on Mutual Information and Artificial Neural Network
The spatial information extraction,screening and input optimization of input variables are the key steps to improve the performance of data-driven hydrological models.In order to improve the effect of data-driven short-term run-off forecasting model,the second National Water Science Numerical Simulation Innovation Competition is taken as an ex-ample.The mutual information and artificial neural network are used to construct a rainfall-runoff model to forecast hour-ly scale runoff.Furthermore,we explored the effect of mutual information on the improvement of rainfall-runoff predic-tion accuracy by artificial neural networks model.The results show that the rainfall-runoff forecasting model based on mutual information and artificial neural network has high simulation accuracy and good applicability,the Nash-Sutcliffe efficiency coefficient is 0.94 and the correlation coefficient square is 0.96 in the validation period.The mutual information method can optimize the input of runoff forecasting model,avoid data redundancy,which can provide a new idea for the selection of runoff forecasting factors in the basin lacking spatial information.
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