1. 基于非线性修正策略的空气质量预警系统研究.
- Author
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王建州 and 杨文栋
- Subjects
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ERROR correction (Information theory) , *HILBERT-Huang transform , *AIR quality indexes , *AIR pollution , *MACHINE learning , *PROCESS optimization , *AIR quality - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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