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

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dc.citedby7
dc.contributor.authorMalek S.en_US
dc.contributor.authorSalleh A.en_US
dc.contributor.authorAhmad S.M.S.en_US
dc.contributor.authorid35069976500en_US
dc.contributor.authorid7003809022en_US
dc.contributor.authorid24721182400en_US
dc.date.accessioned2023-12-29T07:51:24Z
dc.date.available2023-12-29T07:51:24Z
dc.date.issued2010
dc.description.abstractThis 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5445667
dc.identifier.doi10.1109/ICCEA.2010.217
dc.identifier.epage343
dc.identifier.scopus2-s2.0-77952751316
dc.identifier.spage340
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-77952751316&doi=10.1109%2fICCEA.2010.217&partnerID=40&md5=2afced10d66e4dc388de2310f0ba9580
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30691
dc.identifier.volume2
dc.pagecount3
dc.sourceScopus
dc.sourcetitle2010 2nd International Conference on Computer Engineering and Applications, ICCEA 2010
dc.subjectAritificial neural network
dc.subjectChlorophyll-a
dc.subjectFuzzy logic
dc.subjectChlorophyll
dc.subjectComputer applications
dc.subjectDissolution
dc.subjectDissolved oxygen
dc.subjectFuzzy logic
dc.subjectFuzzy systems
dc.subjectPorphyrins
dc.subjectTime series
dc.subjectAlgal biomass
dc.subjectAmmoniacal nitrogen
dc.subjectArtificial Neural Network
dc.subjectChlorophyll a
dc.subjectFeed-forward artificial neural networks
dc.subjectFuzzy logic approach
dc.subjectFuzzy logic model
dc.subjectInput parameter
dc.subjectMalaysia
dc.subjectNitrate nitrogen
dc.subjectPerformance measure
dc.subjectRoot mean square errors
dc.subjectSecchi depth
dc.subjectTime-series data
dc.subjectWater temperatures
dc.subjectNeural networks
dc.titleA comparison between neural network based and fuzzy logic models for chlorophll-a estimationen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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