scholarly journals Short-Term Prediction of Air Pollution in Macau Using Support Vector Machines

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Chi-Man Vong ◽  
Weng-Fai Ip ◽  
Pak-kin Wong ◽  
Jing-yi Yang
Keyword(s):  
Air Pollution ◽  
Support Vector Machines ◽  
Time Series Prediction ◽  
Warning System ◽  
Health Impact ◽  
Support Vector ◽  
Medical Practitioners ◽  
Vector Machines ◽  
Forecasting System ◽  
Short Term Prediction

Forecasting of air pollution is a popular and important topic in recent years due to the health impact caused by air pollution. It is necessary to build an early warning system, which provides forecast and also alerts health alarm to local inhabitants by medical practitioners and the local government. Meteorological and pollutions data collected daily at monitoring stations of Macau can be used in this study to build a forecasting system. Support vector machines (SVMs), a novel type of machine learning technique based on statistical learning theory, can be used for regression and time series prediction. SVM is capable of good generalization while the performance of the SVM model is often hinged on the appropriate choice of the kernel.

scholarly journals Evaluating the performance of support vector machines based on different kernel methods for forecasting air pollutants

2020 ◽  
pp. 5-14
Author(s):  
Adven Masih ◽  
Alexander N. Medvedev
Keyword(s):  
Air Pollution ◽  
Support Vector Machines ◽  
Correlation Coefficient ◽  
Air Pollutants ◽  
Prediction Models ◽  
Pollution Monitoring ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Squared Error ◽  
Vector Machines

The alarming level of air pollution in urban centres is an urgent threat to human health. Its consequences can be measured in terms of health issues experienced by children, an increasing numbers of heart and lung diseases, and, most importantly, the number of pollution related deaths. That is why a lot of attention has recently been paid to air pollution monitoring and prediction modelling. In order to develop prediction models, the study uses Support Vector Machines (SVM) with linear, polynomial, radial base function, normalised polynomial, and Pearson VII function kernels to predict the hourly concentration of pollutants in the air. The paper analyses the monitoring dataset of air pollutants and meteorological parameters as input variable to predict the concentrations of various air pollutants. The prediction performance of the models was assessed by using evaluation metrics, namely the correlation coefficient, root mean squared error, relative absolute error, and relative root squared error. To validate the model, the accuracy of the predictive algorithm was tested against two widely and commonly applied regression approaches called multilayer perceptron and linear regression. Furthermore, back check prediction test was performed to examine the consistency of the models. According to the results, the Pearson VII function and normalised polynomial kernel yield the most accurate results in terms of the correlation coefficient and error values to predict the concentrations of atmospheric pollutants as compared to other SVM kernels and traditional prediction models.

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