基于人工鱼群和分形维数融合SVM的空气质量预测方法

摘要

为了克服现有方法在空气质量预测上存在的缺点，文通过采用改进的离散型人工鱼群算法，并结合分形维，提出基于人工鱼群和分形维融合SVM的空气质量预测方法。首先对人工鱼群算法聚群、觅食行为及移动方式进行离散化改进，引入跳出局部最优策略和并行机制。然后将改进的离散型人工鱼群算法结合分形维，约简空气质量据集。最后采用基于高斯核SVM建立空气质量预测模型。在北京、上海和广州近2年的空气质量据上的实验表明，文方法预测性能较优，具有较高的稳定性和可信性。%To overcome defects of the existing air quality prediction method, an air quality prediction method based on fish swarm and fractal dimension is proposed. Firstly, the artificial fish are processed by discretization, the swarming and foraging behaviors and the moving way are improved, and the parallel mechanism and a strategy for overcoming local optimum are introduced. Secondly, air quality datasets are reduced by the improved discrete artificial fish swarm algorithm and the fractal dimension. Finally, an air quality prediction model is built by using Gaussian kernel SVM. Experiments are conducted on air quality datasets of Beijing, Shanghai and Guangzhou for nearly two years, and the experimental results show the relatively high stability and credibility of the proposed prediction method.