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Predicting shellfish farm closures using time series classification for aquaculture decision support

Citation

Shahriar, MS and Rahman, A and McCulloch, J, Predicting shellfish farm closures using time series classification for aquaculture decision support, Computers and Electronics in Agriculture, 102 pp. 85-97. ISSN 0168-1699 (2014) [Refereed Article]

Copyright Statement

Copyright 2014 Elsevier B.V.

DOI: doi:10.1016/j.compag.2014.01.011

Abstract

Closing a shellfish farm due to pollutants usually after high rainfall and hence high river flow is an important activity for health authorities and aquaculture industries. Towards this problem, a novel application of time series classification to predict shellfish farm closure for aquaculture decision support is investigated in this research. We exploit feature extraction methods to identify characteristics of both univariate and multivariate time series to predict closing or re-opening of shellfish farms. For univariate time series of rainfall, auto-correlation function and piecewise aggregate approximation feature extraction methods are used. In multivariate time series of rainfall and river flow, we consider features derived using cross-correlation and principal component analysis functions. Experimental studies show that time series without any feature extraction methods give poor accuracy of predicting closure. Feature extraction from rainfall time series using piecewise aggregate approximation and auto-correlation functions improve up to 30% accuracy of prediction over no feature extraction when a support vector machine based classifier is applied. Features extracted from rainfall and river flow using cross-correlation and principal component analysis functions also improve accuracy up to 25% over no feature extraction when a support vector machine technique is used. Overall, statistical features using auto-correlation and cross-correlation functions achieve promising results for univariate and multivariate time series respectively using a support vector machine classifier.

Item Details

Item Type:Refereed Article
Keywords:aquaculture decision support;, machine learning, prediction, time series classification
Research Division:Agricultural and Veterinary 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)
Author:Shahriar, MS (Dr Sumon Shahriar)
ID Code:118068
Year Published:2014
Web of Science® Times Cited:1
Deposited By:Information and Communication Technology
Deposited On:2017-07-03
Last Modified:2017-10-17
Downloads:0

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