Open AccessRisk assessment of earthquake network public opinion based on global search BP neural network
Author(s) -
Xing Huang,
Huidong Jin,
Yu Zhang
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0212839
Subject(s) - artificial neural network , computer science , public opinion , risk assessment , convergence (economics) , data mining , principal component analysis , artificial intelligence , computer security , politics , political science , law , economics , economic growth
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.