Hierarchical Bayesian Network Based Incremental Model for Flood Prediction

Abstract

To minimize the negative impacts brought by floods, researchers pay special attention to the problem of flood prediction. In this paper, we propose a hierarchical Bayesian network based incremental model to predict floods for small rivers. The proposed model not only appropriately embeds hydrology expert knowledge with Bayesian network for high rationality and robustness, but also designs an incremental learning scheme to improve the self-improving and adaptive ability of the proposed model. Following the idea of a famous hydrology model, i.e., XAJ model, we firstly present the construction of hierarchical Bayesian network as local and global network construction. After that, we propose an incremental learning scheme, which selects proper incremental data to improve the completeness of prior knowledge and updates parameters of Bayesian network to prevent training from scratch. We demonstrate the accuracy and effectiveness of the proposed model by conducting experiments on a collected dataset with one comparative method.

Yirui Wu

Young Professor, CCF Senior Member

My research interests include Computer Vision, Artifical Intelligence, Multimedia Computing and Intelligent Water Conservancy.