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

Date Published: March 7, 2019

Publisher: Public Library of Science

Author(s): Xing Huang, Huidong Jin, Yu Zhang, Francisco Martínez-Álvarez.

http://doi.org/10.1371/journal.pone.0212839

Abstract

The article proposes a network public opinion risk assessment model for earthquake disasters, which can provide an effective support for emergency departments of China.

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.

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.

The risk assessment method of network public opinion can be used in the practice of earthquake disaster decision.

Partial Text

By June 2018, the number of China’s internet population reached 802 million, and internet popularity is up to 54.3%. The internet popularity provided a convenient for internet user to express their attitudes and views, and their attitudes and views might come into being network public opinion. The opinion spread through network media, more or less, that it can promote or impede the development of the situation, and then the network public opinion of earthquake disaster was a representative of many events. Especially the network public opinion, driven by complex causes, often presents a large number of negative effects, which may cause serious secondary or derivative disasters.

The slow learning speed of BP neural network and the existence of local minimum problems will affect the network’s predication ability [24–26]. To solve these problems, the AGA was used to improve the conventional BP neural network, because the AGA can optimize the network parameters of BP. After that the optimized parameters will be treated as the initial value of the BP algorithm. It can effectively enhance BP neural network’s extrapolation ability, as well as preventing the network from entering partial circulation.

The risk evaluation system of earthquake network public opinion is established that is an essential task for improving the efficiency and capacity of emergency response. This study leads to the following conclusions,

 

Source:

http://doi.org/10.1371/journal.pone.0212839

 

Leave a Reply

Your email address will not be published.