Jiang Wenxin, Zhu Guochang, Shen Yiyun, Xie Qian, Ji Min, Yu Yongtao
Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai'an 223003, China.
Entropy (Basel). 2022 Dec 9;24(12):1803. doi: 10.3390/e24121803.
Air quality has a significant influence on people's health. Severe air pollution can cause respiratory diseases, while good air quality is beneficial to physical and mental health. Therefore, the prediction of air quality is very important. Since the concentration data of air pollutants are time series, their time characteristics should be considered in their prediction. However, the traditional neural network for time series prediction is limited by its own structure, which makes it very easy for it to fall into a local optimum during the training process. The empirical mode decomposition fuzzy forecast model for air quality, which is based on the extreme learning machine, is proposed in this paper. Empirical mode decomposition can analyze the changing trend of air quality well and obtain the changing trend of air quality under different time scales. According to the changing trend under different time scales, the extreme learning machine is used for fast training, and the corresponding prediction value is obtained. The adaptive fuzzy inference system is used for fitting to obtain the final air quality prediction result. The experimental results show that our model improves the accuracy of both short-term and long-term prediction by about 30% compared to other models, which indicates the remarkable efficacy of our approach. The research of this paper can provide the government with accurate future air quality information, which can take corresponding control measures in a targeted manner.
空气质量对人们的健康有着重大影响。严重的空气污染会引发呼吸道疾病，而良好的空气质量则有益于身心健康。因此，空气质量预测非常重要。由于空气污染物的浓度数据是时间序列，在其预测中应考虑其时间特征。然而，传统的用于时间序列预测的神经网络受其自身结构限制，这使得它在训练过程中很容易陷入局部最优。本文提出了基于极限学习机的空气质量经验模式分解模糊预测模型。经验模式分解能够很好地分析空气质量的变化趋势，并获得不同时间尺度下空气质量的变化趋势。根据不同时间尺度下的变化趋势，利用极限学习机进行快速训练，得到相应的预测值。采用自适应模糊推理系统进行拟合，得到最终的空气质量预测结果。实验结果表明，与其他模型相比，我们的模型将短期和长期预测的准确率提高了约30%，这表明我们方法的显著效果。本文的研究可为政府提供准确的未来空气质量信息，使其能够有针对性地采取相应的控制措施。
