Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai
Abstract In recent years, the dangers that air pollutants pose to human health and the environment have received widespread attention. Although accurately predicting the air quality is essential to managing pollution and developing control policies, traditional forecasting models have not been able...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2020-08-01
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| Series: | Aerosol and Air Quality Research |
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| Online Access: | https://doi.org/10.4209/aaqr.2020.05.0247 |
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| author | Hengyuan Liu Guibin Lu Yangjun Wang Nikola Kasabov |
| author_facet | Hengyuan Liu Guibin Lu Yangjun Wang Nikola Kasabov |
| author_sort | Hengyuan Liu |
| collection | DOAJ |
| description | Abstract In recent years, the dangers that air pollutants pose to human health and the environment have received widespread attention. Although accurately predicting the air quality is essential to managing pollution and developing control policies, traditional forecasting models have not been able to simulate the seasonal and diurnal variation in air pollutant concentrations. Furthermore, inadequate processing of the available spatio-temporal data has precluded the capture of predictive historical patterns. Therefore, we have developed a staging evolving spiking neural network (eSNN) model named Staging-eSNN that first employs a time series clustering algorithm to distinguish the seasonal from the diurnal variation in the PM2.5 concentration. We then predict the concentrations in Beijing and Shanghai 1, 3, 6, 12 and 24 hours in advance. Various evaluation indicators show that the Staging-eSNN model achieves higher performance than the support vector regression (SVR), random forest (RF) and other eSNN models. |
| format | Article |
| id | doaj-art-4939186a49434584ba4b688bbbdde074 |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2020-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Aerosol and Air Quality Research |
| spelling | doaj-art-4939186a49434584ba4b688bbbdde0742025-02-09T12:21:40ZengSpringerAerosol and Air Quality Research1680-85842071-14092020-08-0121211510.4209/aaqr.2020.05.0247Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and ShanghaiHengyuan Liu0Guibin Lu1Yangjun Wang2Nikola Kasabov3School of Economics, Shanghai UniversitySchool of Economics, Shanghai UniversitySchool of Environmental and Chemical Engineering, Shanghai UniversitySchool of Engineering, Computing and Mathematical Sciences, Auckland University of TechnologyAbstract In recent years, the dangers that air pollutants pose to human health and the environment have received widespread attention. Although accurately predicting the air quality is essential to managing pollution and developing control policies, traditional forecasting models have not been able to simulate the seasonal and diurnal variation in air pollutant concentrations. Furthermore, inadequate processing of the available spatio-temporal data has precluded the capture of predictive historical patterns. Therefore, we have developed a staging evolving spiking neural network (eSNN) model named Staging-eSNN that first employs a time series clustering algorithm to distinguish the seasonal from the diurnal variation in the PM2.5 concentration. We then predict the concentrations in Beijing and Shanghai 1, 3, 6, 12 and 24 hours in advance. Various evaluation indicators show that the Staging-eSNN model achieves higher performance than the support vector regression (SVR), random forest (RF) and other eSNN models.https://doi.org/10.4209/aaqr.2020.05.0247Air pollutant predictionPM2.5 hourly concentrationSeasonalityEvolving spiking neural networksTime series clustering |
| spellingShingle | Hengyuan Liu Guibin Lu Yangjun Wang Nikola Kasabov Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai Aerosol and Air Quality Research Air pollutant prediction PM2.5 hourly concentration Seasonality Evolving spiking neural networks Time series clustering |
| title | Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai |
| title_full | Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai |
| title_fullStr | Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai |
| title_full_unstemmed | Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai |
| title_short | Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai |
| title_sort | evolving spiking neural network model for pm2 5 hourly concentration prediction based on seasonal differences a case study on data from beijing and shanghai |
| topic | Air pollutant prediction PM2.5 hourly concentration Seasonality Evolving spiking neural networks Time series clustering |
| url | https://doi.org/10.4209/aaqr.2020.05.0247 |
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