Analysis of algal growth using kohonen self organizing feature map (SOM) and its prediction using rule based expert system

Phytoplankton becomes a concern to the environment when it forms dense growth at the water surface, known as algal bloom. However, studies on mechanism of algal bloom are not straight forward mainly caused by uncertainty and complexity of alga ecosystems. This paper describes the analysis of limnolo...

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Main Authors: Malek S., Salleh A., Ahmad S.M.S.
Other Authors: 35069976500
Format: Conference Paper
Published: 2023
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Institution: Universiti Tenaga Nasional
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spelling my.uniten.dspace-296772024-04-17T10:16:13Z Analysis of algal growth using kohonen self organizing feature map (SOM) and its prediction using rule based expert system Malek S. Salleh A. Ahmad S.M.S. 35069976500 7003809022 24721182400 Rule based expert system Self organizing map Blooms (metal) Classifiers Expert systems Gallium alloys Information management Lakes Strength of materials Time series analysis Algal blooms Algal growth Kohonen self-organizing feature map Rule based expert system Rule based expert systems Tropical lakes Water surface Self organizing maps Phytoplankton becomes a concern to the environment when it forms dense growth at the water surface, known as algal bloom. However, studies on mechanism of algal bloom are not straight forward mainly caused by uncertainty and complexity of alga ecosystems. This paper describes the analysis of limnological time-series of Putrajaya Lake and wetlands to determine the growth of alga based on Kohonen self organizing feature maps (SOM). It specifically concentrates on the total Bacillariophyta species due to formation of largest algal composition in the Lake Putrajaya. An expert system was then developed based on the rules extracted from the SOM to model and predict the algal growth. The effectiveness of this system was tested on an actual tropical lake data which yields an acceptable high level of accuracy. � 2009 IEEE. Final 2023-12-28T07:30:45Z 2023-12-28T07:30:45Z 2009 Conference Paper 10.1109/ICIME.2009.63 2-s2.0-70349507021 https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349507021&doi=10.1109%2fICIME.2009.63&partnerID=40&md5=e14cdd2b754c8facdd1c0334a955c554 https://irepository.uniten.edu.my/handle/123456789/29677 5077085 501 504 Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Rule based expert system
Self organizing map
Blooms (metal)
Classifiers
Expert systems
Gallium alloys
Information management
Lakes
Strength of materials
Time series analysis
Algal blooms
Algal growth
Kohonen self-organizing feature map
Rule based expert system
Rule based expert systems
Tropical lakes
Water surface
Self organizing maps
spellingShingle Rule based expert system
Self organizing map
Blooms (metal)
Classifiers
Expert systems
Gallium alloys
Information management
Lakes
Strength of materials
Time series analysis
Algal blooms
Algal growth
Kohonen self-organizing feature map
Rule based expert system
Rule based expert systems
Tropical lakes
Water surface
Self organizing maps
Malek S.
Salleh A.
Ahmad S.M.S.
Analysis of algal growth using kohonen self organizing feature map (SOM) and its prediction using rule based expert system
description Phytoplankton becomes a concern to the environment when it forms dense growth at the water surface, known as algal bloom. However, studies on mechanism of algal bloom are not straight forward mainly caused by uncertainty and complexity of alga ecosystems. This paper describes the analysis of limnological time-series of Putrajaya Lake and wetlands to determine the growth of alga based on Kohonen self organizing feature maps (SOM). It specifically concentrates on the total Bacillariophyta species due to formation of largest algal composition in the Lake Putrajaya. An expert system was then developed based on the rules extracted from the SOM to model and predict the algal growth. The effectiveness of this system was tested on an actual tropical lake data which yields an acceptable high level of accuracy. � 2009 IEEE.
author2 35069976500
author_facet 35069976500
Malek S.
Salleh A.
Ahmad S.M.S.
format Conference Paper
author Malek S.
Salleh A.
Ahmad S.M.S.
author_sort Malek S.
title Analysis of algal growth using kohonen self organizing feature map (SOM) and its prediction using rule based expert system
title_short Analysis of algal growth using kohonen self organizing feature map (SOM) and its prediction using rule based expert system
title_full Analysis of algal growth using kohonen self organizing feature map (SOM) and its prediction using rule based expert system
title_fullStr Analysis of algal growth using kohonen self organizing feature map (SOM) and its prediction using rule based expert system
title_full_unstemmed Analysis of algal growth using kohonen self organizing feature map (SOM) and its prediction using rule based expert system
title_sort analysis of algal growth using kohonen self organizing feature map (som) and its prediction using rule based expert system
publishDate 2023
_version_ 1806428510509596672