Prediction of PM2.5 concentration based on optimized BP neural network with improved sparrow search algorithm

doi:10.13474/j.cnki.11-2246.2022.0292

Abstract

Abstract: Aiming at the problems of slow convergence speed and easy to fall into local extremum of traditional BP neural network model, this paper uses PWLCM(piece wise linear chaotic map)and FA(firefly algorithm)to improve SSA(sparrow search algorithm)and optimize the initial weights and thresholds of the BP neural network model to predict the PM_2.5 concentration in Xi'an. By comparing the evaluation indicators of the prediction results of different models, compared with the better-performing SSA-BP model, the RMSE, MAPE and MAE of the ISSA-BP model prediction results decrease by 3.70, 3.73 and 3.34, respectively. The experimental results show that the improved sparrow search algorithm has efficient global optimal search ability. The optimized ISSA-BP neural network has high prediction stability and accuracy, which is better than BP and SSA-BP neural network models and can be used to predict PM_2.5 concentration.

Key words: sparrow search algorithm, piece wise linear chaotic map, firefly algorithm, BP neural network, PM_2.5 concentration prediction

CLC Number:

P237

ZHAO Kan, SHI Yun, NIU Minjie, WANG Huqin. Prediction of PM_2.5 concentration based on optimized BP neural network with improved sparrow search algorithm[J]. Bulletin of Surveying and Mapping, 2022, 0(10): 44-48,104.

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Prediction of PM_2.5 concentration based on optimized BP neural network with improved sparrow search algorithm

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