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Prediction of daytime variations of HO2 radical concentrations in the marine boundary layer using BP network
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Prediction of daytime variations of HO2 radical concentrations in the marine boundary layer using BP network
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Journal Article
Prediction of daytime variations of HO2 radical concentrations in the marine boundary layer using BP network
2010
Overview
A Back-Propagation Neural Network (BPNN) was established to predict the daytime variations of HO
2
radical concentration observed in the field campaign RISFEX 2003 (RIShiri Fall Experiment 2003) conducted in September 2003 at Rishiri Island (45.07 N, 141.12 E, and 35m asl) in the Sea of Japan. The initial weight matrices and bias vectors for the network were optimized by a bee evolutionary genetic algorithm (BEGA). It was found that the input variables sensitive to HO
2
variation were photolysis frequency of O
3
to O(
1
D) (
J
(O
1
D)), a composite parameter defined as the ratio of HC to NO
x
reactivity towards OH radicals (
Φ
), and the total aerosol surface area (
A
). The predicted results are closely correlated with the experimental data with the coefficient of determination (
R
2
) close to 1. In addition, the means and ranges of the predicted HO
2
concentration agree with the observed data with the correlation coefficient (
R
), the index of agreement (IA) and the fractional bias (FB) in the range of 0.84–0.93, 0.88–0.95 and −14%–7%, respectively. This study demonstrates that BPNN is a potential tool to predict the daytime variations of HO
2
radical concentrations in the marine boundary layer (MBL).
Publisher
SP Science China Press,Springer Nature B.V
Subject
