Method for predicting air quality index (AQI) based on a fusion model
US11816556B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 6, 2023 |
| Grant date | Nov 14, 2023 |
| Priority date | — |
| Expiry date | Jun 6, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method for predicting an air quality index (AQI) based on a fusion model proposes a differential fusion seasonal prediction model (DF-SPM) based on a random forest (RF) model and a convolutional neural network (CNN)-long short-term memory (LSTM)-attention (CLA) model. This method uses the optimal threshold interval (OTI) search algorithm to search and learn the OTI of four seasons during the prediction process, and obtains the final prediction results according to the predicted values of RF model and CLA model. The fusion model combines the prediction advantages of two independent models, and fully considers the seasonal and periodic characteristics of AQI, so as to accurately search OTI in different time periods with the seasonal scale, so as to achieve higher prediction accuracy. The OTI strategy of fusion model is superior to the single threshold strategy, which can extract the historical fluctuation characteristics of AQI and achieve higher prediction accuracy.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.