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An Azure ACES Early Warning System for Air Quality Index Deteriorating
Dong-Her Shih1, Ting-Wei Wu1, Wen-Xuan Liu1
1Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Douliu 640, Taiwan.
International Journal of Environmental Research and Public Health
|November 28, 2019
Summary
This study proposes an air quality index (AQI) warning system using machine learning on the Azure cloud. The Linear Regression (LR) algorithm accurately predicted air quality for the next 1-3 hours, aiding public health protection.
Area of Science:
- Environmental Science
- Computer Science
- Public Health
Background:
- Industrialization and urbanization have worsened air pollution, impacting public health.
- Accurate air quality monitoring and forecasting are crucial for mitigating health risks.
Purpose of the Study:
- To develop an air quality index (AQI) warning system using cloud computing.
- To evaluate machine learning algorithms for real-time air quality prediction.
Main Methods:
- Implemented an AQI warning system on the Azure cloud platform.
- Compared Decision Forest Regression (DFR), Neural Network Regression (NNR), and Linear Regression (LR) algorithms.
- Utilized the best-performing algorithm to predict 6 key air pollutants for AQI calculation.
Main Results:
- The Linear Regression (LR) algorithm demonstrated superior performance in predicting air quality.
- The proposed system accurately forecasts AQI for the subsequent one to three hours.
Conclusions:
- The developed air quality index (AQI) warning system provides effective short-term air quality predictions.
- This system can help prevent public health hazards and reduce healthcare costs associated with air pollution.
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