Establishment of Ultrafine Dust Prediction Dashboard Prototype
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Research Objectives
- Establishment of a dashboard prototype to effectively communicate ultrafine dust prediction results
- Prediction of ultrafine dust based on data collected in real-time (every hour)
- After comparing the prediction performance of each machine learning models, the prediction value and prediction performance of the best model are presented.
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Research Methods
- Real-time air pollution data collection and preprocessing using Air Korea's Open API
- Pre-train machine learning algorithms such as MLP, RNN, LSTM, CNN, etc., by optimizing calculation speed and using them for real-time predictions.
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Development of Ultrafine Dust Prediction Dashboard
- Posting the webpage of the ultrafine dust dashboard app(http://keibigdata.github.io/Service.html)
- Output of the best-performing prediction model. Providing hourly forecasts, data sources, and dashboard descriptions
- We can check ultrafine dust forecast information and take countermeasures quickly.