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Shellfish farm closure prediction and cause identification using machine learning methods


Rahman, A and D'Este, C, Shellfish farm closure prediction and cause identification using machine learning methods, Computers and Electronics in Agriculture, 110 pp. 241-248. ISSN 0168-1699 (2015) [Refereed Article]

Copyright Statement

Crown Copyright 2014 Published by Elsevier B.V.

DOI: doi:10.1016/j.compag.2014.11.023


Shellfish farms are needed to be closed if they are contaminated during their production as otherwise it may lead to serious health hazard. The authorities monitor a number of water quality variables to check the health of shellfish farms and to decide on the closure of the farms. The research presented in this paper aims to automate this process by developing novel algorithms to identify the cause of closure and also predicting the closure. As the frequency of closure is relatively very small, the labelled data sets are imbalanced in nature. We have developed a novel ensemble feature ranking algorithm that explicitly deals with class imbalance problem and identifies the cause of closure. We have also presented a class balancing ensemble classifier to predict shellfish farm closure. The class balancing ensemble classifier predicts closure/opening with as high as 71.69% accuracy and achieves best balancing act with decision tree base classifier in 75% locations. Rain and salinity are found to be the key causes of closure and the causality depends of the properties of the locations.

Item Details

Item Type:Refereed Article
Research Division:Agricultural, Veterinary and Food Sciences
Research Group:Fisheries sciences
Research Field:Fisheries management
Objective Division:Animal Production and Animal Primary Products
Objective Group:Fisheries - aquaculture
Objective Field:Aquaculture crustaceans (excl. rock lobster and prawns)
UTAS Author:D'Este, C (Dr Claire D'Este)
ID Code:117989
Year Published:2015
Web of Science® Times Cited:7
Deposited By:Zoology
Deposited On:2017-06-30
Last Modified:2017-10-16

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