Prediction of PM2.5 Concentration Based on WPA-WOA-BP Neural Network
Abstract Aiming at issues such as slow learning speed and ease of falling into local extremum existing in the BP neural network, using the mixed algorithm of whale optimization algorithm(WOA) and wolf pack algorithm(WPA) to optimize the weights and the thresholds of BP neural network, we construct the WPA-WOA-BP neural network model and apply it to predict PM2.5 concentration. The experimental results prove the stability and feasibility of applying the WPA-WOA-BP neural network model to predict PM2.5 concentration. The precision of this model is higher than BP neural network, WPA-BP neural network and WOA-BP neural network.
Key words :
prediction of PM2.5 concentration
neural network
WPA
WOA
Cite this article:
XIE Shaofeng,ZHAO Yun,LI Guohong et al. Prediction of PM2.5 Concentration Based on WPA-WOA-BP Neural Network[J]. jgg, 2021, 41(1): 12-16.
XIE Shaofeng,ZHAO Yun,LI Guohong et al. Prediction of PM2.5 Concentration Based on WPA-WOA-BP Neural Network[J]. jgg, 2021, 41(1): 12-16.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2021/V41/I1/12
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