Hourly Ground-level Ozone Concentration Prediction using Support Vector Regression
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Publication Details
Author list: Chaiyakhan K., Chujai P., Kerdprasop N., Kerdprasop K.
Publication year: 2017
Volume number: 2227
Start page: 306
End page: 311
Number of pages: 6
ISBN: 9789881404732
ISSN: 2078-0958
Languages: English-Great Britain (EN-GB)
Abstract
High concentrations ground-level ozone is a harmful air pollutant that affects human, animals, and plants. Breathing ground-level ozone can activate a diversity of health problems, especially for the elderly, children, and people who have asthma. Ground-level ozone can also have dangerous results on vegetation and crops. The purposed of this research is to build the support vector regression model for predicting the hourly ground-level ozone concentration. On the model building Pearson correlation is used to find the relationship between ozone, which is a dependent variable, and several independent variables such as temperature, relative humidity, nitrogen dioxide and carbon monoxide. The air pollutant and meteorological data since 2012 to 2015 had been collected at the northern air quality station in urban area of warm climate from the pollution control department, Chiang Mai, Thailand. The results from correlation analysis show that temperature has the highest positive relationship with ozone, whereas relative humidity has the highest negative relationship with ozone. We use k-means clustering as a tool to categorize ozone into three groups and then assign weight for each group. After that, we apply normalization to convert ozone, temperature, and relative humidity values to be on a same scale. In the training and testing processes, we use normalized data and cluster weight as inputs of the model. In the evaluation phase, we compare the predictive performance of support vector regression and multiple linear regression models based on the three metrics: root mean squared error, index of agreement, and mean absolute percentage.
Keywords
K-means clustering, Ozone prediction, Support vector regression, Urban air pollutant
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