The estimation of microphysical parameters of pollution (effective radius and complex refractive index) from optical aerosol parameters entails a complex problem. In previous work based on machine learning techniques, Artificial Neural Networks have been used to solve this problem. In this paper, the use of a classification and regression solution based on the k-Nearest Neighbor algorithm is proposed. Results show that this contribution achieves better results in terms of accuracy than the previous work.
CITATION STYLE
Llerena, C., Müller, D., Adams, R., Davey, N., & Sun, Y. (2018). Estimation of microphysical parameters of atmospheric pollution using machine learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11139 LNCS, pp. 579–588). Springer Verlag. https://doi.org/10.1007/978-3-030-01418-6_57
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