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

journal contribution
posted on 2023-05-19, 07:39 authored by Rahman, A, Shahriar, MS
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.

History

Publication title

International Journal of Computational Intelligence and Applications

Volume

12

Article number

1350008

Number

1350008

Pagination

1-19

ISSN

1469-0268

Department/School

School of Information and Communication Technology

Publisher

Imperial College Press

Place of publication

United Kingdom

Rights statement

Copyright 2013 Imperial College Press

Repository Status

  • Restricted

Socio-economic Objectives

Assessment and management of terrestrial ecosystems

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    University Of Tasmania

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