Proposal to Supplement the Missing Values of Air Pollution Levels in Meteorological Dataset

Abstract

Recently, various air pollution factors have been measured and analyzed to reduce damages caused by it. In this process, many missing values occur due to various causes. To compensate for this, basically a vast amount of training data is required. This paper proposes a statistical techniques that effectively compensates for missing values generated in the process of measuring ozone, carbon dioxide, and ultra-fine dust using a small amount of learning data. The proposed algorithm first extracts a group of meteorological data that is expected to have positive effects on the correction of missing values through statistical information analysis such as the correlation between meteorological data and air pollution level factors, p-value, etc. It is a technique that efficiently and effectively compensates for missing values by analyzing them. In order to confirm the performance of the proposed algorithm, we analyze its characteristics through various experiments and compare the performance of the well-known representative algorithms with ours.