THESIS
2022
1 online resource (x, 45 pages) : illustrations (chiefly color)
Abstract
Air pollution forecast has become critical because of its direct impact on human health
and the increased production of air pollutants caused by rapid industrialization. Machine
learning (ML) solutions are being drastically explored in this domain because of their
potential to produce highly accurate results with access to historical data. However, experts
in the environmental area are skeptical about adopting ML solutions in real-world
applications and policy-making due to their black-box nature. In contrast, despite having
low accuracy sometimes, the existing traditional simulation models (e.g., CMAQ) are
widely used and follow well-defined and transparent equations. Therefore, presenting
the knowledge learned by the ML model can make it transparent as well as comprehensible.
In addit...[
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Air pollution forecast has become critical because of its direct impact on human health
and the increased production of air pollutants caused by rapid industrialization. Machine
learning (ML) solutions are being drastically explored in this domain because of their
potential to produce highly accurate results with access to historical data. However, experts
in the environmental area are skeptical about adopting ML solutions in real-world
applications and policy-making due to their black-box nature. In contrast, despite having
low accuracy sometimes, the existing traditional simulation models (e.g., CMAQ) are
widely used and follow well-defined and transparent equations. Therefore, presenting
the knowledge learned by the ML model can make it transparent as well as comprehensible.
In addition, validating the ML model’s learning with the existing domain knowledge
might aid in addressing the expert’s skepticism, building appropriate trust, and better
utilizing ML models. In collaboration with three experts having an average of five years' research experience in the air pollution domain, we identified that feature (meteorological
feature like wind) contribution towards the final forecast as the vital information to be verified with domain knowledge. In addition, the performance of the ML model compared
with the traditional simulation model and visualization of raw wind trajectories are essential
for domain experts to validate the feature contribution information. We designed
and developed AQX, a visual analytics system to help experts validate and verify the ML
model’s learning with their domain knowledge based on the identified information. The
system includes coordinated multiple views to present the contributions of input features
at different levels of aggregation in both temporal and spatial dimensions. It also provides
a performance comparison of ML and traditional models in terms of accuracy and spatial
map, along with the animation of raw wind trajectories for the input period. We further
demonstrated two case studies and conducted expert interviews with two domain experts
to show the effectiveness and usefulness of AQX.
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