Hierarchical Bayesian Network Based Incremental Model for Flood Prediction

  • Yirui Wu
  • Weigang Xu
  • Qinghan Yu
  • Jun FengEmail author
  • Tong Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11295)


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.


Incremental learning Hierarchical Bayesian network Flood prediction 



This work was supported by National Key R&D Program of China under Grant 2018YFC0407901, the Natural Science Foundation of China under Grant 61702160, Grant 61672273 and Grant 61832008, the Fundamental Re-search Funds for the Central Universities under Grant 2016B14114, the Science Foundation of Jiangsu under Grant BK20170892, the Science Foundation for Distinguished Young Scholars of Jiangsu under Grant BK20160021, Scientific Foundation of State Grid Corporation of China (Research on Ice-wind Disaster Feature Recognition and Prediction by Few-shot Machine Learning in Transmission Lines), and the open Project of the National Key Lab for Novel Software Technology in NJU under Grant K-FKT2017B05.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yirui Wu
    • 1
    • 2
  • Weigang Xu
    • 1
  • Qinghan Yu
    • 1
  • Jun Feng
    • 1
    Email author
  • Tong Lu
    • 2
  1. 1.College of Computer and InformationHohai UniversityNanjingChina
  2. 2.National Key Lab for Novel Software TechnologyNanjing UniversityNanjingChina

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