LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran
Z Ghaemi1, A Alimohammadi1, M Farnaghi2,3
1Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, No. 1346, ValiAsr Street, Mirdamad cross, Tehran, 19967-15433, Iran.
Environmental Monitoring and Assessment
|April 22, 2018
Summary
This study introduces an online algorithm for urban air quality prediction, significantly improving speed while maintaining accuracy. The system accurately forecasts air pollutant concentrations for real-time urban air quality management.
Area of Science:
- Environmental Science
- Data Science
- Urban Planning
Background:
- Urban air pollution poses critical risks, necessitating accurate prediction and monitoring.
- Predicting air pollutant concentrations is complex due to dynamic, spatio-temporal variability influenced by historical data and weather.
- Conventional methods like Support Vector Machine (SVM) and Artificial Neural Networks (ANN) struggle with large volumes of streaming data for urban air pollution prediction.
Purpose of the Study:
- To develop and evaluate a spatio-temporal system for improved urban air pollution prediction in Tehran.
- To overcome the limitations of conventional methods in handling large, dynamic datasets for air quality forecasting.
- To enhance the real-time spatial and temporal prediction capabilities for urban air quality.
Main Methods:
- A spatio-temporal system utilizing a Least Squares Support Vector Machine (LaSVM)-based online algorithm was designed.
- Continuous feeding of pollutant concentration, meteorological, and geographical data into the online forecasting system.
- Performance evaluation by comparing the Air Quality Index (AQI) predictions of the online system against a traditional SVM algorithm.
Main Results:
- The online algorithm demonstrated a significant increase in speed compared to the traditional SVM classifier.
- Accuracy of the SVM classifier was preserved by the online algorithm.
- Hourly predictions for the next 24 hours showed an overall accuracy of 0.71, a root mean square error of 0.54, and a coefficient of determination of 0.81.
Conclusions:
- The developed online algorithm is practically useful for real-time spatial and temporal prediction of urban air quality.
- The system offers a viable solution for improving the speed and accuracy of air pollution forecasting in urban environments.
- The findings indicate the potential for enhanced urban air quality management through advanced data-driven forecasting methods.
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