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Spatial-temporal prediction of algal bloom


Shahriar, S and Rahman, A, Spatial-temporal prediction of algal bloom, Proceedings, ICNC 2013, 23-25 July 2013, Shenyang, China, pp. 973-977. ISBN 978-1-4673-4714-3 (2013) [Non Refereed Conference Paper]

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DOI: doi:10.1109/ICNC.2013.6818117


We present an application of spatial-temporal prediction to track algal blooms. Algal bloom is an important water quality events in marine, coastal and estuarine environments. For a day, we first identify an area with anomalous algal growth represented by spatial points in the gridded data where values of Chlorophyll-a (indicator for algal bloom) are above a threshold chosen by domain scientists. To represent the shape of the algal bloom area, we create convex hull from spatial gridded points. We then find the radii from centroid of the convex hull. The radii are further used as features in predicting spatial region of the algal bloom using regression techniques. We also predict the centroid (represented in latitude and longitude) of an algal bloom area to track whether bloom area is moving. Experimental results show that our approach can reasonably predict algal bloom area and its centroid one day ahead using features from previous day. The prediction technique benefits towards decision support systems for aquaculture industry and environmental departments.

Item Details

Item Type:Non Refereed Conference Paper
Keywords:spatial-temporal prediction, algal bloom, prediction techniques
Research Division:Information and Computing Sciences
Research Group:Computer vision and multimedia computation
Research Field:Pattern recognition
Objective Division:Animal Production and Animal Primary Products
Objective Group:Fisheries - aquaculture
Objective Field:Fisheries - aquaculture not elsewhere classified
UTAS Author:Shahriar, S (Dr Sumon Shahriar)
ID Code:116730
Year Published:2013
Deposited By:Information and Communication Technology
Deposited On:2017-05-17
Last Modified:2017-05-17

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