This document describes a project to establish an integrated water monitoring system in Dublin Bay using multiple sensors and visual sensing technologies. The goals are to improve water quality monitoring, identify security threats and health hazards, and produce baseline water quality datasets. Sensors have been deployed at sites in Dublin Bay to continuously measure water parameters. Over 500,000 sensor measurements and 2.5 million images have been collected. Data analysis uses machine learning methods to detect events like turbidity increases and predict variables. The network has improved understanding of Dublin Bay, but challenges remain regarding coverage, biofouling, costs and translating data into actionable information.

1. A Smart City-Smart Bay Project:
Establishing an integrated water monitoring system for decision
support in Dublin Bay
Fiona Regan, Timothy Sullivan, Ciprian Briciu, Helen Cooney, Dian Zhang*, Edel
O’Connor*, Noel O’Connor*, Alan Smeaton*
Marine and Environmental Sensing Technology Hub (MESTECH), National Centre for Sensor Research
Dublin City University
*CLARITY Centre for Sensor Web Technologies, Dublin City University
Dublin, Ireland

2. Project
Ra+onale
Design,
deployment
and
integra2on
of
an
autonomous
real-­‐2me
mul2modal
sensing
network
for
improved
decision
making
Research
Objec+ves
• Improve
Water
quality
monitoring
• Improve
discrete
sampling
regimes
• Iden+fy
and
Improve
detec+on
of
Security
threats
• Iden2fy
threats
to
health
(microbial
and
other
pollutants)
• Enhanced
Signal
processing:
Develop
surrogate
measurements
• Produce
Baseline
datasets
on
water
quality
Introduc+on
Ra+onale
Study
site
Methods
Instrumenta+on
Data
analysis
Results
Conclusions

4. Current
and
future
Network
Distribu+on
by
2014
River
Liffey
Dublin
Bay
Dublin
City
Centre
2
km

5. Pilot
Sites:
Malahide
and
Poolbeg
Estuaries

6.
Introduc+on
Ra+onale
Study
site
Methods
Instrumenta+on
Data
analysis
Results
Conclusions

7. In-­‐situ
sensors
• Mul+-­‐parameter
sondes
equipped
with
real-­‐+me
telemetry
systems
• IP66-­‐Rated
outdoor
network
camera
• Ini+al
systems
deployed
in
October
2010
-­‐
August
2013:
• Circa
2.5
million
images
have
been
collected
• Circa
500,000
individual
sensor
measurements
Introduc+on
Ra+onale
Study
site
Methods
Instrumenta+on
Data
analysis
Results
Conclusions

8.
Introduc+on
Ra+onale
Study
Site
Methods
Instrumenta+on
Data
analysis
Results
Conclusions
Duc+ng
of
marina
structure
220V
power
supply
Commercial
telemetry
solu+on
box

9.
Data
Analy+cs
• Machine
learning
objec+ves:
automated
detec+on
and
trajectory
of
vessels
• Automated
Turbidity
event
detec+on
–
pixel-­‐based
adap+ve
segmenter
method
• Salinity
predic+on
using
mul+ple
data
sources
(+de,
flow,
weather
data)
using
regression
tree
approach
• Shipping
ac+vity
+
turbidity:
predic+on
of
sampling
+mes
and
microbial
contamina+on
–
separa+ng
natural
events
from
anthropogenic
events
• Water
level
predic+on
• Security
Threats:
Unauthorized
shipping
Introduc+on
Ra+onale
Methods
Study
Site
Instrumenta+on
Data
analysis
Results
Conclusions

10. 1 Aug
2 Aug
3 Aug
4 Aug
5 Aug
6 Aug
7 Aug
0
5
10
15
20
25
30
Turbidity 2 m
Turbidity 4 m
Turbidity(NTU)
Date 2012
Detec+ng
and
automa+ng
turbidity
event
detec+on
Introduc+on
Ra+onale
Methods
Study
Site
Instrumenta+on
Data
analysis
Results
Conclusions

17. Conclusions
• An
extensive
network
of
both
in-­‐situ
aqua+c
sensors
and
visual
sensing
systems
have
been
and
are
in
process
of
deployment
in
Dublin
Bay
• The
network
has
already
had
demonstrable
impact
on
monitoring
and
understanding
dynamic
processes
in
Dublin
Bay
• Incorpora+on
of
visual
sensing
nodes
into
the
network
has
proven
advantageous
• Machine
learning
and
increased
compu+ng
power
has
aided
in
data
analysis
–
future
work
will
emphasize
data
analy+cs
• Challenges
remain:
Increased
spa+al
coverage,
Biofouling!,
Cost,
Transla2on
of
data
into
knowledge

18. Thank
You!
Ques+ons?
Contacts:
+m.sullivan@dcu.ie;
fiona.regan@dcu.ie