Water & Wastewater Asia July/August 2022

SINGAPORE FOCUS
audio data are used as the model’s
input to determine if it is a false-positive
signature. ANN is a computational
model of interconnected nodes loosely
based on the biological neural network
structure. It is a supervised ML method
known for its ability to learn mapping
functions of underlying features from
a given labelled training dataset. In
addition, a trained ANN model is
Potential falsepositive
alarm
acoustic data with
the Y- and X-axis
representing the
frequency and time
range respectively.
The intensity of the
audio signature is
represented by the
image colour
This low-power circuit can process
the stream of data coming from
the acoustic sensor in real-time
or periodically. Once an acoustic
signature with frequencies-of-interest
is detected, the circuit will notify the
microcontroller for further investigation
of the acoustic event.
These frequencies-of-interest
represent the band of frequencies
that are associated with pipeline
leaks. Therefore, the circuit will
use a bandpass filter to remove the
unwanted frequencies and a timer
circuit to check the consistency of the
acoustic signature. The charging will
alert the microcontroller if the acoustic
signature containing the frequenciesof-interest
persists over some time.
Otherwise, the timer circuit will reset
the charging circuit and monitor the
signal again for another fixed period.
determining if the acoustic signature
is an actual pipeline leak based on
previously collated data.
The ML algorithm is designed and
trained based on the data collected
during the project duration to weed
out potential acoustic data that can
potentially trigger false-positive alerts
such as the audio signature of a train
passing underneath a buried water
pipeline or the piling works from a
nearby construction site.
The ML algorithm used is an artificial
neural network (ANN) model, collected
capable of capturing unknown, complex
and non-linear relationships for a variety
of application areas, ranging from image
classification to speech recognition.
The ANN architecture selected in
this work has a topological network
with two hidden layers. For this
selected ANN architecture, the
number of neurons for hidden layers
1 and 2 is dependent on the size of
the training dataset. This particular
architecture is chosen for its ability
to learn underlying patterns from a
large number of distinct data samples
using a comparatively small number of
hidden neurons.
This acoustic signature is then
transmitted to the IoT cloud platform
where an ML algorithm is used to
determine if the acoustic signature
is an actual pipeline leak based on
previously collated data. Once the
microcontroller is alerted due to a
possible leak event, the system will
record the acoustic signature for a
fixed interval and send this data to the
cloud over a low-power IoT network for
further analysis. This additional layer
of analysis in the cloud will leverage a
customised ML algorithm to lower the
occurrence of false-positive alerts by
A two-hidden-layer ANN architecture where ‘I’ represents the number of features for each
training sample, ‘S’ represents the number of output neurons and ‘N’ represents the number of
training samples. The number of neurons used for hidden layers 1 and 2 respectively is denoted
as ‘J’ and ‘K’ respectively
WATER & WASTEWATER ASIA | JULY/AUGUST 2022 11