Name
Abstract | ||
|---|---|---|
This paper proposes a novel algorithm based on cloud model granulation (CMG) for air quality forecasting. Through data exploration of three different types of monitoring localities in Wuhan City, the determinative pollutants were reduced to NO2, PM10, O3, and PM25 for modeling. After iterative granulation of original time series, the concepts of cloud model were extracted for each granule from original data space to feature space. Then, the cloud model features of future granules were predicted in the new feature space. Finally, the value in the feature space is transformed into the solution in the concept space. In addition, this paper uses the grid search to optimize the parameters in all experiments. Compare with several machine learning approaches, considering the mean squared error, the results on composition model and direct model shows that the proposed algorithm has better in predicting both individual air quality index and air quality index. At ZKX locality, the CMG algorithm can achieve high accuracy 71.43% for prediction of air quality index class. The results show that this algorithm not only can simplify the modeling process of uncertain time series in the form of knowledge abstraction, but also has good prediction performance in IAQI and AQI. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s13638-018-1116-3 | EURASIP Journal on Wireless Communications and Networking |
Keywords | Field | DocType |
Cloud model,Soft computing,Machine learning,Uncertainty,Air quality forecasting | Data mining,Hyperparameter optimization,Locality,Feature vector,Computer science,Mean squared error,Real-time computing,Air quality index,Granulation,Soft computing,Cloud computing | Journal |
Volume | Issue | ISSN |
2018 | 1 | 1687-1499 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
4 | ||