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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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 |
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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 |
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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 |
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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. |
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35069976500 |
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35069976500 Malek S. Salleh A. Ahmad S.M.S. |
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Conference Paper |
author |
Malek S. Salleh A. Ahmad S.M.S. |
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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 |
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2023 |
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1806425760254132224 |
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13.18916 |