Abstract: One of the key steps in artificial neural networks (ANN) forecasting is the determination of
significant input variables. A partial mutual information (PMI) method was used to characterize the
dependence of a potential model between its input and output variables. We also developed a copula entropy
method for effective calculation of mutual information (MI) and PMI, and verified its accuracy and
performance using numerical tests. This forecasting technique has been applied to a real-world case study of
the Three Gorges reservoir (TGR), and results show that the proposed method is useful and effective for
identification of suitable inputs of flood forecasting model.