Methods for imputation of missing values in air quality data sets

• Main Authors: Chi-Lan Hung, 洪啟嵐
• Other Authors: 望熙榮
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/11587976008880936667

碩士 === 國立中興大學 === 環境工程學系所 === 96 === In order to discuss the problem of the air quality, to understand the pollution and the different pollution transmission in each place, and to design strategies to control and improve the contamination, Environmental Protection Administration in Taiwan in 1994 divided Taiwan into 7 air quality districts based on their different pollution features, the terrain, and the climate in each district. They have already set up over 70 air quality monitoring stations in Taiwan. Because of the air quality monitoring stations which monitored air quality condition of each place, we are able to have the historical information of air quality of everywhere for everybody to research and find out the useful information from this huge database. But around 10% of data are missing only during the transmission process. However, the missing values of the database will affect the data analysis, hence, it is very important to resolve the missing value problem. My research uses Inverse Square Distance Weighting method and Kriging method to impute the missing values, and discusses, analyzes, and compares the result of using the 2 different methods. Monte Carlo method is then used to test and verify which one is the better method to yield the accurate values to replace the missing values. After my research, the absolute error of Inverse Square Distance Weighting is smaller than it of Monte Carlo method for imputation of air quality data. After verifying, the absolute error of Inverse Square Distance Weighting and Kriging method is respectively 25% and 19%. It shows that Kriging method is better imputation method than Inverse Square Distance Weighting.