Learning a Typhoon Bayesian Network Structure from Natural Language Reports

Zhangrui Yao, Junhan Chen, Yinghui Pan*, Yifeng Zeng, Biyang Ma, Zhong Ming

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Given the huge toll caused by natural disasters, it is critically important to develop an effective disaster management and emergency response technique. In this article, we investigate relationships between typhoon-related variables and emergency response from natural language (NL) reports. A major challenge is to exploit typhoon state information for typhoon contingency plan generation, especially from unstructured text data based on NL input. To tackle this issue, we propose a novel framework for learning typhoon Bayesian network structures (FLTB), which can extract typhoon state information from unstructured NL, mine inter-information causal relationships and then generate Bayesian networks. We first extract information about typhoon states through NL processing (NLP) techniques, and then analyze typhoon reports by designing heuristic rules to identify causal relationships between states. We leverage these features to improve the learned structures and provide user-interaction mechanisms to finalize Bayesian networks. We evaluate the performance of our framework on real-world typhoon datasets and develop the Bayesian networks based typhoon emergency response systems.

Original languageEnglish
Title of host publicationIntelligence Science IV
Subtitle of host publication5th IFIP TC 12 International Conference, ICIS 2022, Proceedings
EditorsZhongzhi Shi, Yaochu Jin, Xiangrong Zhang
Place of PublicationSwitzerland
Number of pages9
ISBN (Electronic)9783031149030
ISBN (Print)9783031149023, 9783031149054
Publication statusPublished - 19 Oct 2022
Event5th IFIP TC 12 International Conference on Intelligence Science, ICIS 2022 - Xi'an, China
Duration: 28 Oct 202231 Oct 2022

Publication series

NameIFIP Advances in Information and Communication Technology
Volume659 IFIP
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X


Conference5th IFIP TC 12 International Conference on Intelligence Science, ICIS 2022


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