基于非线性修正策略的空气质量预警系统研究

建立科学、有效的空气质量预警系统,对于保护人们的身体健康和促进社会和谐稳定具有重要的科学价值和实际意义.为此,本文首先利用孤立森林(isolationforest,iForest)算法进行空气质量指数(air quality index,AQI)离群点分析,然后建立了一种空气质量预警系统,该系统由数据预处理模块、优化模块、预测模块和修正模块构成,融合了时变滤波经验模态分解(time varying filtering based empirical mode decomposition,TVF-EMD)、改进的蝴蝶优化算法(modified butterfly optimization algorithm,MBOA)、离群鲁棒极限学习机(outlier robust extreme learning machine,ORELM)和非线性修正策略,该预警系统成功地实现了空气质量的有效预警.同时,以污染程度不同的5个城市作为实验地点对预警效果进行检验,结果表明:1)与经验模态分解(empirical mode decomposition,EMD)相比,TVF-EMD可以更为有效地降低原始数据的非线性和非平稳性特征;2)基于MBOA的误差非线性修正策略比其他误差修正策略更胜一筹,可以显著提高预警系统的性能;3)建立的预警系统的性能要优于其他对比模型,可以对污染程度不同的城市进行有效预警.

Air quality early warning system based on nonlinear correction strategy

Developing scientific and effective air quality early warning system has important scientific and practical significance for protecting people's physical health and promoting social harmony and stability. Therefore, the isolation forest (iForest) algorithm is employed to analyze the outlier data in original air quality index (AQI) data, and then an effective air quality early warning system is developed in this paper. The system is consists of four modules:data preprocessing module, optimization module, forecasting module and correction module, which combines time varying filtering based empirical mode decomposition (TVF-EMD), modified butterfly optimization algorithm (MBOA), outlier robust extreme learning machine (ORELM), and nonlinear correction strategy, and successfully achieves effective early warning for air quality. In order to verify the effectiveness of the developed air quality early warning system, five cities with different air pollution level in China are employed for empirical research. The results show that:1) Compared with empirical mode decomposition (EMD), TVF-EMD can be more effective to reduce the nonlinear and non-stationary features of the original data; 2) The developed MBOA-based error nonlinear correction strategy is superior to other error correction strategies, which can significantly improve the performance of the early warning system; 3) The performance of the developed system is superior to other compared methods, which can provide effective early warning for cities with different air pollution level.