基于分形流形学习的支持向量机空气污染指数预测模型

Air Pollution Index Prediction Model of SVM Based on Fractal Manifold Learning

针对目前北京、上海和广州地区较严重空气污染问题,建立了基于分形流形学习的支持向量机空气污染指数预测模型.首先采用分形理论计算出空气污染数据集分形维数;其次根据分形维数,采用流形学习将高维空气污染数据集通过非线性映射嵌入到低维空间中,对空气污染数据集进行降维;最后建立基于高斯核的支持向量机预测模型对三地区空气污染指数进行预测.北京、上海和广州三地空气污染指数预测结果表明,该模型较传统预测模型,预测性能更优,具有良好的稳定性和有效性.

Air pollution index prediction model of SVM based on fractal manifold learning was proposed, for the serious air pollution of Beijing, Shanghai and Guangzhou areas at present. Firstly, the intrinsic dimension of air pollution data set is attained using fractal dimension;Secondly, the high dimension air pollution data set is embedded into a low-dimensional space using nonlinear mapping of manifold learning based on fractal dimension, which can reduce the dimension of the set;Finally, air pollution index prediction model of SVM based on Gaussian kernel function is built, which is applied in forecasting the air pollution index. Experimental results on the three data sets show that the prediction model is superior to other traditional models, and that it has high stability and effectiveness.