Classification of Continuous Sky Brightness Data Using Random Forest

Sky brightness measuring and monitoring are required to mitigate the negative effect of light pollution as a byproduct of modern
civilization. Good handling of a pile of sky brightness data includes evaluation and classification of the data according to its quality
and characteristics such that further analysis and inference can be conducted properly. )is study aims to develop a classification
model based on Random Forest algorithm and to evaluate its performance. Using sky brightness data from 1250 nights with
minute temporal resolution acquired at eight different stations in Indonesia, datasets consisting of 15 features were created to train
and test the model. )ose features were extracted from the observation time, the global statistics of nightly sky brightness, or the
light curve characteristics. Among those features, 10 are considered to be the most important for the classification task. )e model
was trained to classify the data into six classes (1: peculiar data, 2: overcast, 3: cloudy, 4: clear, 5: moonlit-cloudy, and 6: moonlit-
clear) and then tested to achieve high accuracy (92%) and scores (F-score � 84% and G-mean � 84%). Some misclassifications
exist, but the classification results are considerably good as indicated by posterior distributions of the sky brightness as a function
of classes. Data classified as class-4 have sharp distribution with typical full width at half maximum of 1.5 mag/arcsec 2 , while
distributions of class-2 and -3 are left skewed with the latter having lighter tail. Due to the moonlight, distributions of class-5 and
-6 data are more smeared or have larger spread. )ese results demonstrate that the established classification model is reasonably
good and consistent