Internet of things solution for intelligent air pollution prediction and visualization
Date Issued
2019-07-01
Author(s)
Risteska Stojkoska, Biljana
Korunoski, Mladen
Abstract
Air pollution monitoring and control is becoming
a key priority in urban areas due to its substantial effect on
human morbidity and mortality. This paper presents a system
architecture for intelligent pollution visualization and future pollution prediction by encompassing pollution measurements and
meteorological parameters. First, a pollution model using spatial
interpolation is built. By adding meteorological parameters this
model is further used to identify the pollution field evolution
and the position of potential sources of air pollution. Using
deep learning techniques, the system provides predictions for
future pollution levels as well as times to reaching alarming
thresholds. The whole system is encompassed in a fast, easy to
use web service and a client that visually renders the system
responses. The system is built and tested on data for the city
of Skopje. Although the spatial resolution of the system data is
low, the results are satisfactory and promising. Since the system
can be seamlessly deployed on an Internet of Things sensing
architecture, the improved data spatial resolution will improve
performance.
a key priority in urban areas due to its substantial effect on
human morbidity and mortality. This paper presents a system
architecture for intelligent pollution visualization and future pollution prediction by encompassing pollution measurements and
meteorological parameters. First, a pollution model using spatial
interpolation is built. By adding meteorological parameters this
model is further used to identify the pollution field evolution
and the position of potential sources of air pollution. Using
deep learning techniques, the system provides predictions for
future pollution levels as well as times to reaching alarming
thresholds. The whole system is encompassed in a fast, easy to
use web service and a client that visually renders the system
responses. The system is built and tested on data for the city
of Skopje. Although the spatial resolution of the system data is
low, the results are satisfactory and promising. Since the system
can be seamlessly deployed on an Internet of Things sensing
architecture, the improved data spatial resolution will improve
performance.
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