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We present a dynamic data-driven decision support for aquaculture farm closure. In decision support, we use machine learning techniques in predicting closures of a shellfish farm. As environmental time series are used in closure, we propose two approaches using time series and machine learning for closure prediction. In one approach, we consider time series prediction and then using expert rules to predict closure. In other approach, we use time series classification for closure prediction. Both approaches exploit a dynamic data-driven technique where prediction models are updated with the update of new data to predict closure decisions. Experimental results at a case study shellfish farm validate the applicability of the proposed method in aquaculture decision support.

Item Type:	Refereed Article
Keywords:	aquaculture decision support, dynamic data-driven decision support, machine learning
Research Division:	Agricultural, Veterinary and Food Sciences
Research Group:	Fisheries sciences
Research Field:	Aquaculture
Objective Division:	Animal Production and Animal Primary Products
Objective Group:	Fisheries - aquaculture
Objective Field:	Aquaculture crustaceans (excl. rock lobster and prawns)
UTAS Author:	Shahriar, MS (Dr Sumon Shahriar)
ID Code:	118044
Year Published:	2014
Web of Science® Times Cited:	3
Deposited By:	Information and Communication Technology
Deposited On:	2017-07-03
Last Modified:	2017-10-17
Downloads:	131 View Download Statistics