Get access

Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data

Authors

  • Lianfa Li,

    1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Resources Research, Chinese Academy of Sciences, Beijing, China.
    2. Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
    Search for more papers by this author
  • Jinfeng Wang,

    1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Resources Research, Chinese Academy of Sciences, Beijing, China.
    Search for more papers by this author
  • Hareton Leung,

    1. Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
    Search for more papers by this author
  • Chengsheng Jiang

    1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Resources Research, Chinese Academy of Sciences, Beijing, China.
    Search for more papers by this author

Address correspondence to Lianfa Li, LREIS, Institute of Geographical Sciences and Resources Research, Chinese Academy of Sciences, Rm. 1305, No. All, Road Datun, Anwai, District Chaoyang, Beijing, China 100101; lspatial@gmail.com.

Abstract

Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine-tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross-validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.

Ancillary