Risk assessment of earthquake network public opinion based on global search BP neural network

Xing Huang, Huidong Jin and Yu Zhang

PLOS ONE, 2019, vol. 14, issue 3, 1-14

Abstract: Background: The article proposes a network public opinion risk assessment model for earthquake disasters, which can provide an effective support for emergency departments of China. Method: It uses the accelerated genetic algorithm (AGA) to improve BP neural network. The main contents: This article selects 10 indexes by using the methods of the principal component analysis (PCA) and cumulative contribution (CC) to assess the risk of the earthquake network public opinion. The article designs a BP algorithm to measure the risk degree of the earthquake network public opinion and uses AGA to improve the BP model for parameter optimization. Results: The experiment results of the improved BP model shows that its global error is 7.12×10, and the error is reduced to 22.35%, which showed the improving BP model has advantages in convergence speed and evaluation accuracy. Conclusion: The risk assessment method of network public opinion can be used in the practice of earthquake disaster decision.

Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0212839

DOI: 10.1371/journal.pone.0212839

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