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

doi:10.16182/j.issn1004731x.joss.201805015

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.

Key words: forest fire danger grading, meteorological factors, PSO-GA-BP neural network, Forest fire forecast

CLC Number:

O657.7

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.

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