| Idea Name: |
Awair |
| Slogan: |
Air Quality Monitoring and Prediction |
| Supervisor Name: |
Rafia Mumtaz |
| Supervisor Designation: |
Assistant Professor |
| Supervisor School: |
School of Electrical Engineering and Computer Science |
| Supervisor Department: |
SEECS |
| Contact number: |
03425563343 |
| Email ID: |
[email protected] |
| Abstract: |
| Our idea proposes to make a efficient air quality monitoring and prediction system based on satellite imagery and deep learning techniques. |
| What is the unmet need in society that your idea will fulfill ? |
| Pakistan being 2nd in the index of most polluted countries, with Lahore and Karachi being one of the top ten most polluted cities, we need a solution to this problem and the related unawareness. We can overcome this by timely forecasting air quality and then taking efficient measures. |
| Who needs it ? How many would benefit ? |
| The air quality monitoring and prediction system will help generate public awareness. The results will be used by environment management and regularity units to monitor air pollution and hence take effective measures to manage it. The project will be initially tested for Lahore and its air quality. |
| How will the solution works |
| We will be using satellite imagery for data retrieval and well-trained deep learning models to forecast air quality. This will be done by implementing a decision support system that will be capable of analyzing the air quality parameters and subsequently generate air quality classification maps. With the help of this analysis and prediction, expert evaluation and early warning could be provided to the users. All of this will be implemented on a web portal for public viewing. |
| Who are your competitors ? How is your solution different |
| Most of the air quality monitoring and prediction systems are based on ground monitoring system. The greater precision and coverage of the satellite imagery yield more detailed air quality parameters as compared to the ground monitoring stations. |
| Status: |
new |
| Entry Date & Time: |
2021-01-01 (1835) |
