scholarly journals Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model

2017 ◽  
Vol 14 (3) ◽  
pp. 249 ◽  
Author(s):  
Jianzhou Wang ◽  
Tong Niu ◽  
Rui Wang
Keyword(s):  
Support Vector Machine ◽  
Air Quality ◽  
Least Squares ◽  
Early Warning ◽  
Early Warning System ◽  
Cloud Model ◽  
Warning System ◽  
Support Vector

Power System Early Warning Mechanism based on Least Squares Support Vector Machine

2013 ◽  
Vol 8 (9) ◽  
pp. 577-584
Author(s):  
Luo Zhikun ◽  
Liu Xiaoxiao ◽  
Guo Xinze ◽  
Qi Wenhui ◽  
Lian Guohai ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Power System ◽  
Least Squares ◽  
Early Warning ◽  
Support Vector ◽  
Early Warning Mechanism

scholarly journals A Novel Air Quality Early-Warning System Based on Artificial Intelligence

2019 ◽  
Vol 16 (19) ◽  
pp. 3505
Author(s):  
Mo ◽  
Zhang ◽  
Li ◽  
Qu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Prediction Model ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Air Pollutant ◽  
Concentration Data ◽  
Whale Optimization ◽  
The World

The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

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