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Algae growth prediction through identification of influential environmental variables: A machine learning approach

Citation

Rahman, A and Shahriar, MS, Algae growth prediction through identification of influential environmental variables: A machine learning approach, International Journal of Computational Intelligence and Applications, 12, (2) Article 1350008. ISSN 1469-0268 (2013) [Refereed Article]


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Abstract

In this paper, we present an approach for predicting algae growth through the selection of influential environmental variables. Chlorophyll a is considered to be an indicator for algal biomass and we predict this as a proxy for algae growth. Environmental variables like water temperature, salinity, etc. have influence upon algae growth. Depending on the geographic location, the influence of these environmental variables will vary. Given a set of relevant environmental variables we perform feature selection using a number of algorithms to identify the variables relevant to the growth. We have developed an influence matrix-based approach to select the relevant features. The selected features are then used for predicting algae growth using different regression algorithms to identify their relative strength. The approach is tested on the algae data of Derwent estuary in Tasmania. The experimental results demonstrate that the accuracy of algae growth prediction with influence matrix-based feature selection is superior to using all the features.

Item Details

Item Type:Refereed Article
Keywords:algae bloom prediction, algae growth prediction, ensemble classifier
Research Division:Environmental Sciences
Research Group:Environmental Science and Management
Research Field:Environmental Monitoring
Objective Division:Environment
Objective Group:Ecosystem Assessment and Management
Objective Field:Ecosystem Assessment and Management of Coastal and Estuarine Environments
Author:Shahriar, MS (Dr Sumon Shahriar)
ID Code:118643
Year Published:2013
Deposited By:Computing and Information Systems
Deposited On:2017-07-17
Last Modified:2017-07-17
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