Ozone Peak and Pollution Forecasting Using Support Vectors
نویسندگان
چکیده
This paper investigates the efficiency of Support Vector Machines for ozone peak and air pollution forecasting. A specific methodology adapted to the data has been proposed. The method is based on regression estimation of the ozone concentration for a given day. Then for the classification problem (deciding whether that day is polluted or not), the regression value is stacked. Model selection problem is tackled using automatic hyperparameters tuning. Results show that the overall method performs well and can be a promising alternative for non-linear modelisation of ozone pollution.
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