Open AccessAPPLICATION OF MACHINE LEARNING TO FILL IN THE MISSING MONITORING DATA OF AIR QUALITY
Author(s) -
Mac Duy Hung
Publication year - 2018
Publication title -
vietnam journal of science and technology/science and technology
Language(s) - English
Resource type - Journals
eISSN - 2815-5874
pISSN - 2525-2518
DOI - 10.15625/2525-2518/56/2c/13036
Subject(s) - artificial neural network , missing data , autoregressive–moving average model , support vector machine , autoregressive model , air quality index , machine learning , viet nam , computer science , kriging , artificial intelligence , data mining , statistics , mathematics , meteorology , geography , economy , economics
In this paper, three machine learning models have been applied to predict and fill in the missing monitoring data of air quality for Gia Lam and Nha Trang stations in Hanoi and Khanh Hoa respectively, including Autoregressive Moving Average (ARMA), Artificial Neural Network (ANN), and Support Vector Regression (SVR). Two air pollutants being NO2 and PM10 were selected for this study. The experimental results showed that the performance of all three studied models is better than that of some traditional approaches, including Multiple Linear Regression (LR) and Spline interpolation. Besides that, ARMA, ANN and SVR can capture the fluctuation of concentrations of the selected pollutants. These results indicated that the machine learning is a feasible approach to deal with the missing of data which is one of the biggest problems of air quality monitoring stations in Viet Nam.