Speaker
Description
Air pollution has been linked to several health problems in-
cluding heart disease, stroke and lung cancer. Modelling and
analyzing this dependency requires reliable and accurate air
pollutant measurements collected by stationary air monitor-
ing stations. However, usually only a low number of such
stations are present within a single city. To retrieve pollution
concentrations for unmeasured locations, researchers rely on
land use regression (LUR) models. Those models are typi-
cally developed for one pollutant only. However, as results
in different areas have shown, modelling several related out-
put variables through multi-task learning can improve the
prediction results of the models significantly.
In this work, we compared prediction results from single-
task and multi-task learning multilayer perceptron models
on measurements taken from the OpenSense dataset and the
London Atmospheric Emissions Inventory dataset. LUR fea-
tures were generated from OpenStreetMap using OpenLUR
and used to train hard parameter sharing multilayer per-
ceptron models. The results show multi-task learning with
sufficient data significantly improves the performance of a
LUR model.
| ML method | Feed forward neural network |
|---|
