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A comparison between neural network based and fuzzy logic models for chlorophll-a estimation

This paper describes the application of two novel computational methods such as fuzzy logic and supervised artificial neural network (ANN) to model algal biomass in tropical Putrajaya Lake and Wetlands (Malaysia). Limnological time series data collected from 2001 until 2004 was utilized using input...

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Main Authors:	Malek S., Salleh A., Ahmad S.M.S.
Other Authors:	35069976500
Format:	Conference Paper
Published:	2023
Subjects:	Aritificial neural network Chlorophyll-a Fuzzy logic Chlorophyll Computer applications Dissolution Dissolved oxygen Fuzzy systems Porphyrins Time series Algal biomass Ammoniacal nitrogen Artificial Neural Network Chlorophyll a Feed-forward artificial neural networks Fuzzy logic approach Fuzzy logic model Input parameter Malaysia Nitrate nitrogen Performance measure Root mean square errors Secchi depth Time-series data Water temperatures Neural networks
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id	my.uniten.dspace-30691
record_format	dspace
spelling	my.uniten.dspace-306912024-04-18T10:58:37Z A comparison between neural network based and fuzzy logic models for chlorophll-a estimation Malek S. Salleh A. Ahmad S.M.S. 35069976500 7003809022 24721182400 Aritificial neural network Chlorophyll-a Fuzzy logic Chlorophyll Computer applications Dissolution Dissolved oxygen Fuzzy logic Fuzzy systems Porphyrins Time series Algal biomass Ammoniacal nitrogen Artificial Neural Network Chlorophyll a Feed-forward artificial neural networks Fuzzy logic approach Fuzzy logic model Input parameter Malaysia Nitrate nitrogen Performance measure Root mean square errors Secchi depth Time-series data Water temperatures Neural networks This paper describes the application of two novel computational methods such as fuzzy logic and supervised artificial neural network (ANN) to model algal biomass in tropical Putrajaya Lake and Wetlands (Malaysia). Limnological time series data collected from 2001 until 2004 was utilized using input parameters such as water temperature, pH, secchi depth, dissolved oxygen, ammoniacal nitrogen and nitrate nitrogen. Performance measure for the models developed was in terms of root mean square error (RMSE). Both models developed gave similar result with models developed using fuzzy logic approach performed slightly better compared to feed-forward artificial neural network model. � 2010 IEEE. Final 2023-12-29T07:51:24Z 2023-12-29T07:51:24Z 2010 Conference Paper 10.1109/ICCEA.2010.217 2-s2.0-77952751316 https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952751316&doi=10.1109%2fICCEA.2010.217&partnerID=40&md5=2afced10d66e4dc388de2310f0ba9580 https://irepository.uniten.edu.my/handle/123456789/30691 2 5445667 340 343 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	Aritificial neural network Chlorophyll-a Fuzzy logic Chlorophyll Computer applications Dissolution Dissolved oxygen Fuzzy logic Fuzzy systems Porphyrins Time series Algal biomass Ammoniacal nitrogen Artificial Neural Network Chlorophyll a Feed-forward artificial neural networks Fuzzy logic approach Fuzzy logic model Input parameter Malaysia Nitrate nitrogen Performance measure Root mean square errors Secchi depth Time-series data Water temperatures Neural networks
spellingShingle	Aritificial neural network Chlorophyll-a Fuzzy logic Chlorophyll Computer applications Dissolution Dissolved oxygen Fuzzy logic Fuzzy systems Porphyrins Time series Algal biomass Ammoniacal nitrogen Artificial Neural Network Chlorophyll a Feed-forward artificial neural networks Fuzzy logic approach Fuzzy logic model Input parameter Malaysia Nitrate nitrogen Performance measure Root mean square errors Secchi depth Time-series data Water temperatures Neural networks Malek S. Salleh A. Ahmad S.M.S. A comparison between neural network based and fuzzy logic models for chlorophll-a estimation
description	This paper describes the application of two novel computational methods such as fuzzy logic and supervised artificial neural network (ANN) to model algal biomass in tropical Putrajaya Lake and Wetlands (Malaysia). Limnological time series data collected from 2001 until 2004 was utilized using input parameters such as water temperature, pH, secchi depth, dissolved oxygen, ammoniacal nitrogen and nitrate nitrogen. Performance measure for the models developed was in terms of root mean square error (RMSE). Both models developed gave similar result with models developed using fuzzy logic approach performed slightly better compared to feed-forward artificial neural network model. � 2010 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	A comparison between neural network based and fuzzy logic models for chlorophll-a estimation
title_short	A comparison between neural network based and fuzzy logic models for chlorophll-a estimation
title_full	A comparison between neural network based and fuzzy logic models for chlorophll-a estimation
title_fullStr	A comparison between neural network based and fuzzy logic models for chlorophll-a estimation
title_full_unstemmed	A comparison between neural network based and fuzzy logic models for chlorophll-a estimation
title_sort	comparison between neural network based and fuzzy logic models for chlorophll-a estimation
publishDate	2023
_version_	1806425760254132224
score	13.211869

A comparison between neural network based and fuzzy logic models for chlorophll-a estimation

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