PMID- 30845195
OWN - NLM
STAT- MEDLINE
DCOM- 20191212
LR  - 20191217
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 14
IP  - 3
DP  - 2019
TI  - Risk assessment of earthquake network public opinion based on global search BP 
      neural network.
PG  - e0212839
LID - 10.1371/journal.pone.0212839 [doi]
LID - e0212839
AB  - 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 7.12x10, 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.
FAU - Huang, Xing
AU  - Huang X
AUID- ORCID: 0000-0003-4048-2473
AD  - School of Management, Southwest University of Science and Technology, Mianyang, 
      China.
FAU - Jin, Huidong
AU  - Jin H
AD  - CSIRO Data61, Canberra ACT, Australia.
FAU - Zhang, Yu
AU  - Zhang Y
AD  - School of Management, Southwest University of Science and Technology, Mianyang, 
      China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20190307
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - China
MH  - Decision Making
MH  - Disaster Planning/*methods
MH  - *Earthquakes
MH  - Humans
MH  - Neural Networks, Computer
MH  - Principal Component Analysis
MH  - *Public Opinion
MH  - Risk Assessment/methods
MH  - *Social Networking
PMC - PMC6405059
COIS- The authors have declared that no competing interests exist.
EDAT- 2019/03/08 06:00
MHDA- 2019/12/18 06:00
PMCR- 2019/03/07
CRDT- 2019/03/08 06:00
PHST- 2018/04/17 00:00 [received]
PHST- 2019/02/12 00:00 [accepted]
PHST- 2019/03/08 06:00 [entrez]
PHST- 2019/03/08 06:00 [pubmed]
PHST- 2019/12/18 06:00 [medline]
PHST- 2019/03/07 00:00 [pmc-release]
AID - PONE-D-18-11519 [pii]
AID - 10.1371/journal.pone.0212839 [doi]
PST - epublish
SO  - PLoS One. 2019 Mar 7;14(3):e0212839. doi: 10.1371/journal.pone.0212839. 
      eCollection 2019.