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| Research on PCA-PSO-SVM ozone prediction considering spatial-temporal features |
| DONG Hong-zhao1, WANG Le-heng1, TANG Wei2, YANG Qiang3, SHE Yi-ni1 |
1. Intelligent Traffic System Joint Research Institute, Zhejiang University of Technology, Hangzhou 310014, China;
2. Hangzhou Institute of Environment Sciences, Hangzhou 310014, China;
3. Hangzhou Huanyan Technology Co., Ltd. Hangzhou 310014, China |
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Abstract The current ozone predicting method usually lacks the effect of the spatial covering of ozone pollution coupled with its strong self-correlation within a certain period. To compensate such a deficiency, a PCA-PSO-SVM based model of ozone combining prediction considering spatial-temporal features was proposed. Using wavelet analysis and system clustering, the fluctuation characteristics in time series and spatial distributing similarity of the ozone was extracted, and the maximum daily 8-hour average concentration of the ozone was predicted resorting to PCA-PSO-SVM model which composes of the principal component analysis (PCA) and particle swarm optimization based support vector machine (PSO-SVM). The model was verified by the experiment with the historical data of atmospheric pollutants and meteorological situation from 2016 to 2018 in urban Hangzhou. The results showed that the ozone predicting accuracy by the PCA-PSO-SVM model considering spatial-temporal features was significantly improved. Compared with the PCA-PSO-SVM model without spatial-temporal features, the predicting accuracy was raised 19%. The experiment also proved that the temperature exerts the largest influence among all the meteorological factors on the ozone prediction. The proposed model was showed its robustness to obtain high predicting accuracy even in case of the weak weather forecast.
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Received: 15 June 2020
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