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Prediction of Aerosol Particle Size Distribution Based on Neural Network
- Source :
- Advances in Meteorology, Vol 2020 (2020)
- Publication Year :
- 2020
- Publisher :
- Hindawi Limited, 2020.
-
Abstract
- Aerosol plays a very important role in affecting the earth-atmosphere radiation budget, and particle size distribution is an important aerosol property parameter. Therefore, it is necessary to determine the particle size distribution. However, the particle size distribution determined by the particle extinction efficiency factor according to the Mie scattering theory is an ill-conditioned integral equation, namely, the Fredholm integral equation of the first kind, which is very difficult to solve. To avoid solving such an integral equation, the BP neural network prediction model was established. In the model, the aerosol optical depth obtained by sun photometer CE-318 and kernel functions obtained by Mie scattering theory were used as the inputs of the neural network, particle size distributions collected by the aerodynamic particle sizer APS 3321 were used as the output, and the Levenberg–Marquardt algorithm with the fastest descending speed was adopted to train the model. For verifying the feasibility of the prediction model, some experiments were carried out. The results show that BP neural network has a better prediction effect than that of the RBF neural network and is an effective method to obtain the aerosol particle size distribution of the whole atmosphere column using the data of CE-318 and APS 3321.
- Subjects :
- Atmospheric Science
Article Subject
010504 meteorology & atmospheric sciences
Artificial neural network
Mie scattering
05 social sciences
Fredholm integral equation
01 natural sciences
Pollution
Integral equation
Aerosol
Sun photometer
symbols.namesake
Geophysics
Meteorology. Climatology
0502 economics and business
symbols
Particle
Applied mathematics
Particle size
QC851-999
050203 business & management
0105 earth and related environmental sciences
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 16879317 and 16879309
- Volume :
- 2020
- Database :
- OpenAIRE
- Journal :
- Advances in Meteorology
- Accession number :
- edsair.doi.dedup.....3237b56e3e9fee19999a60ae2d102822