Abstract:
"
Spreading awareness about the regional air quality has become a much-needed
objective for many developing countries due to reported unhealthy air quality levels in
highly urbanised areas. Lack of robust air quality monitoring networks can lead to lesser
awareness about the regional air quality levels creating public health risks. Air
pollutants such as particulate matter and aerosols derivatives of these agents cause
serious health risks. Particulate matter and aerosols reduce visibility by changing how
the electromagnetic waves are absorbed, reflected, and refracted. Various sensors of
earth-orbiting Satellites can produce images of the earth’s surface. Particles in the
atmosphere can scatter the light and changes the extinction properties of the formed
satellite images. Thus, satellite images can be used as a surrogate to assess the aerosols
and particulate matter in the air. Recent advancements in artificial intelligence have
led to innovations that utilise machine learning and deep learning, which creates an
ideal environment for modelling Air Quality using satellite data. Thus, this project aims
to combine timely available satellite imagery with deep learning techniques to monitor
air quality levels in different areas in Sri Lanka. According to the EPA (The
Environmental Protection Agency) standards, a system was designed, developed, and
evaluated to measure air quality using Satellite images and available ground-based data.
The study showed low satisfactory result directly using Geo raster data into the deep
learning model. Nonetheless, changing the satellite products and the layer by pre processing the image gave changes to the prediction result accuracy. Therefore, the
research project developed easy management and developed a baseline system by
separating machine learning and analysis part for future research work. Hens this
approach is a novel experimental approach in this area of research. "
