Local and Global Bayesian Network based Model for Flood Prediction

To minimize the negative impacts brought by floods, researchers from pattern recognition community pay special attention to the problem of flood prediction by involving technologies of machine learning. In this paper, we propose to construct hierarchical Bayesian network to predict floods for small rivers, which appropriately embed hydrology expert knowledge for high rationality and robustness. We present the construction of the hierarchical Bayesian network in two stages comprising local and global network construction. During the local network construction, we firstly divide the river watershed into small local regions. Following the idea of a famous hydrology model - the Xinanjiang model, we establish the entities and connections of the local Bayesian network to represent the variables and physical processes of the Xinanjiang model, respectively. During the global network construction, intermediate variables for local regions, computed by the local Bayesian network, are coupled to offer an estimation for time-varying values of flow rate by proper inferences of the global network. At last, we propose to improve the output of Bayesian network by utilizing former flow rate values. We demonstrate the accuracy and robustness of the proposed method by conducting experiments on a collected dataset with several comparative methods.