Applying machine learning to estimate the optical properties of black carbon fractal aggregates. (August 2018)

• Record Type: Journal Article
• Authors: Luo, Jie; Zhang, Yongming; Wang, Feng; Wang, Jinjun; Zhang, Qixing
• Is Part Of: Journal of quantitative spectroscopy & radiative transfer. Volume 215(2018)
• Page Start: 1
• Page End: 8
• Publication Date: 2018-08
• Subjects: Black carbon aggregates -- Optical properties -- Support vector machine -- Morphology -- T-matrix; Spectrum analysis -- Periodicals; Radiation -- Periodicals; Analyse spectrale -- Périodiques; Rayonnement -- Périodiques; Radiation; Spectrum analysis; Periodicals; 543.0858
• DOI: 10.1016/j.jqsrt.2018.05.002
• Languages: English
• ISSNs: 0022-4073

Abstract

Highlights: The applicability of SVM for estimating integral optical properties of BC is evaluated. The relative errors between MSTM and SVM predicted are acceptable. Successful prediction with less training data for large aggregates can be achieved. Abstract: Calculation of the optical properties of black carbon fractal aggregates is important in a variety of applications, whereas the complex morphology of black carbon aggregates makes it particularly computationally expensive for broadband applications. Previous studies have tried to parameterize the optical properties with empirical fitting. In this work, a machine learning method, support vector machine, was applied to estimate the optical properties. The integral optical properties obtained by numerically exact multiple-sphere T-matrix method and trained support vector machine model respectively, were presented and discussed in this study. The comparative results show excellent agreement between the two methods. Even though machine learning may provide inaccurate results when morphological parameters are beyond the range of training dataset, after adding small number of data on the basis of pre-trained support vector machine model to cover the full range, relative errors of extinction efficiency ( Qext ), absorption efficiency ( Qabs ), scattering efficiency ( Qsca ) and asymmetric factor ( ASY ) are within 2.3%, 1.4%, 5% and 4.8% respectively. The errors are acceptable because the MSTM results may fluctuate from mean values over a small range. Therefore, machine learning can reconstruct the full range of optical properties by small number of training data. This work provides a new method to estimate optical properties of black carbon aggregates and is helpful for simplifying the calculation of optical properties of black carbon fractal aggregates. It may be helpful for atmospheric models calculations, aerosol optical inversions and other fields involved in calculation of BC aggregates with broadband morphological parameters. Moreover, it may provide a new insight for parameterizations of optical properties of BC with more complex morphologies.