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Wave Height Estimation Using a Novel Seaweed-Attached Sensor

Authors:
Masoud Emam
Caroline Press
Hamed Jafarzadeh
Marco Belcastro
Brendan O’Flynn
Joanne Casserly
Frank Kane

Keywords: Seaweed attached sensor; Aquaculture; Underwater sensor; Embedded system; Kalman filter; Artificial Neural Network

Abstract:
The growth rate of seaweed is significantly affected by wave parameters and sea conditions. The wave characteristics in an aquaculture farm is normally measured using expensive equipment, which is not affordable for many farmers or researchers, and is not easily relocated from place to place to evaluate wave conditions in a variety of locations. In this paper, a sensor fusion method is presented which can estimate wave height using the data logged by a multi modal low-cost seaweed-attached sensor system. The sensor was developed for use in an Aquaculture scenario. This method is based on combination of extended Kalman filter and artificial neural networks. Regarding the importance of studying the impact of wave on seaweeds growth rate, this method will avail many researchers to use wave height data in their study to fill the gap in knowledge of the impact of water motion on aquaculture and maximising of seaweed harvests.

Pages: 28 to 31

Copyright: Copyright (c) IARIA, 2021

Publication date: November 14, 2021

Published in: conference

ISSN: 2308-4405

ISBN: 978-1-61208-917-1

Location: Athens, Greece

Dates: from November 14, 2021 to November 18, 2021