Design and Study of Forest Fire Forecasting Based on PSO and GA-BP Neural Network

Authors

Shuhua Bai, Nanchang Institute of Technology, Nanchang 330044, China;
Mingxing Kuang, Nanchang Institute of Technology, Nanchang 330044, China;

Abstract

Abstract: This paper collects the 2013-2015 meteorological data of four stations (Nanchang, Jingdezhen, Gi’an, Ganzhou) in Jiangxi province, and the corresponding forest fire danger grading, to establish a neural network fire prediction model. Corresponding network model is built respectively by adopting the genetic algorithm, particle swarm optimization (PSO) algorithm and particle swarm genetic hybrid algorithm to optimize the BP neural network. By comparing the prediction results of BP network, GA-BP network, PSO-BP network and PSO-GA-BP network with the experiments data, it shows that the PSO-GA-BP network prediction model is of higher accuracy, the PSO and GA enjoy the best optimization effect.

Recommended Citation

Bai, Shuhua and Kuang, Mingxing (2019) "Design and Study of Forest Fire Forecasting Based on PSO and GA-BP Neural Network," Journal of System Simulation: Vol. 30: Iss. 5, Article 15.
DOI: 10.16182/j.issn1004731x.joss.201805015
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss5/15

First Page

1739

Revised Date

2017-07-25

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201805015

Last Page

1748

CLC

O657.7

Recommended Citation

Bai Shuhua, Kuang Mingxing. Design and Study of Forest Fire Forecasting Based on PSO and GA-BP Neural Network[J]. Journal of System Simulation, 2018, 30(5): 1739-1748.

DOI

10.16182/j.issn1004731x.joss.201805015