Monthly discharge prediction with 2D cloud model in non-dry season

The cloud model is transformation model between qualitative concepts and quantitative expression, the qualitative concept is represented by expected value, entropy and hyper entropy in this mode. It combines the fuzziness with randomness, and possesses strong robustness for the uncertain question. Considering the main influence of factors, the 2D cloud model is applied to the prediction of monthly discharge in non-dry season, this mechanism can effectively overcome the fuzziness and the random in monthly discharge prediction, and can offer a novel approach for monthly discharge prediction. The results show that this method has excelled precision, it is also in accord with the real ones.