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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, MSIn 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 ApplicationsVolume
12Article number
1350008Number
1350008Pagination
1-19ISSN
1469-0268Department/School
School of Information and Communication TechnologyPublisher
Imperial College PressPlace of publication
United KingdomRights statement
Copyright 2013 Imperial College PressRepository Status
- Restricted