Using Support Vector Machine Method with Spatio-temporal Features to Predict Urban Air Quality
Date Issued
2016
Date
2016
Author(s)
Liu, Chih-Chun
Abstract
Urban air quality prediction has been considered imperative because it allows citizens to properly respond to poor air quality according to the forecasts. Compared to transport models, statistical methods, usually referring to machine learning, have been more and more popular for air quality prediction in this decade owing to their time-saving and easy-to-use characteristics. However, limited to difficulty in data acquisition, combination of temporal and spatial prediction is still inconclusive. This study aims to utilize support vector machine (SVM), a machine learning algorithm, to predict air quality of unknown space and time with temporal and spatial features extracted by Geographic information system (GIS). The Northern Air Basin of Taiwan was selected as study site; 20 monitoring stations were chosen as training stations (also reference stations) while the rest of 5 stations were testing stations, whose data were not involved in training process. Temporal prediction was first executed in the reference stations, and then the predicted air quality index (AQI) were used for spatial inference to obtain the future AQI of unknown locations. The verification revealed high accuracy of future AQI (the next one hour) prediction with low root mean squared error (RMSE) under 4; nonetheless, higher RMSE over 10 was calculated in spatial inference stage. The performance of spatial inference in winter was noticeably better than the performance in other three seasons probably due to the low spatial divergence of air quality in winter. This “spatio-temporal air quality prediction” is found slightly inaccurate in comparison to other machine learning methods demonstrated in other studies by viewing normalized RMSE; nevertheless, this proposed method is able to conduct spatial prediction, while others can only predict air quality temporally. Despite the fact that the spatial inference only own acceptable accuracy and some obstacles still remain, the framework is feasible in practice with the controlled errors. Further application, like better policy making or more delicate forecasts announced by mobile devices, may be realized under this framework.
Subjects
Air quality prediction
Support vector machine
Machine learning
Spatio-temporal features
Geographic information system
SDGs
Type
thesis
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