Use machine learning technique to retrieve air quality data
Project Description

As an emerging technique, machine learning algorithms can capture the complex nonlinear relationships with interactions. Compared to the traditional statistical method, machine learning is more reliable for the air pollution analysis. PM2.5 and O3 are the two secondary ambient pollutants which can exert adverse health effects on the human beings. In this study, we plan to combine machine learning techniques (BP neural network and convolutional neural network), ground observation, satellite data to generate high resolution PM2.5/O3 products for the Pearl River Delta region.

Supervisor
FUNG Jimmy Chi Hung
Co-Supervisor
LU Xingcheng
Quota
3
Course type
UROP1000
UROP1100
UROP2100
UROP3100
UROP4100
Applicant's Roles

The student needs to learn various machine learning techniques

Applicant's Learning Objectives

By completing this project, students would have basic understand of air quality forecasting and its uncertainty.

Complexity of the project
Moderate