A variation reasoning Bayesian neural network-based flood ensemble forecasting method

The invention discloses a variation reasoning Bayesian neural network-based flood ensemble forecasting method. The method comprises the following steps of: setting dimensions of each layer of a Bayesian neural network; Selecting the prior probability distribution of the weight parameters of the Bayesian neural network, and parameterizing the weight parameters of the Bayesian neural network throughthe variational parameters to approximate the posterior probability distribution of the weight parameters of the Bayesian neural network; Calculating the relative entropy of the prior probability distribution and the variation posterior probability distribution, and calculating an expected log-likelihood function according to the training data set; Constructing an objective function according tothe relative entropy and the expected log-likelihood function; maximizing a target function, and training variational reasoning parameters; And carrying out ensemble forecasting on unknown flood by using the trained variationa