Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jher.2013.04.003
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dc.titleHybrid ANN-GA model for predicting turbidity and chlorophyll-a concentrations
dc.contributor.authorMulia, I.E.
dc.contributor.authorTay, H.
dc.contributor.authorRoopsekhar, K.
dc.contributor.authorTkalich, P.
dc.date.accessioned2014-11-26T10:26:38Z
dc.date.available2014-11-26T10:26:38Z
dc.date.issued2013-12
dc.identifier.citationMulia, I.E., Tay, H., Roopsekhar, K., Tkalich, P. (2013-12). Hybrid ANN-GA model for predicting turbidity and chlorophyll-a concentrations. Journal of Hydro-Environment Research 7 (4) : 279-299. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jher.2013.04.003
dc.identifier.issn15706443
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/110872
dc.description.abstractThe transport and fate of admixtures at coastal zones are driven, or at least modulated, by currents. In particular, in tide-dominated areas due to higher near-bottom shear stress at strong currents, sediment concentration and turbidity are expected to be at maximum during spring tide, while algal growth rate likely is peaking up at slack currents during neap tide. Varying weather and atmospheric conditions might modulate the said dependencies, but the water quality pattern still is expected to follow the dominant tidal cycle. As tidal cycling could be predicted well ahead, there is a possibility to use water quality and hydrodynamic high-resolution data to learn past dependencies, and then use tidal hydrodynamic model for nowcasting and forecasting of selected water quality parameters. This paper develops data driven models for nowcasting and forecasting turbidity and chlorophyll-a using Artificial Neural Network (ANN) combined with Genetic Algorithm (GA). The use of GA aims to automate and enhance ANN designing process. The training of the ANN model is done by constructing input-output mapping, where hydrodynamic parameters act as an input for the network, while turbidity and chlorophyll-a are the corresponding outputs (desired target). Afterward, the prediction is carried out only by employing computed water surface elevation as an input for the trained ANN model. The proposed data driven model has successfully revealed complex relationships and utilized its experiential knowledge acquired from the training process for facilitating the subsequent use of the data driven model to yield an accurate prediction. © 2013 International Association for Hydro-environment Engineering and Research, Asia Pacific Division.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.jher.2013.04.003
dc.sourceScopus
dc.subjectArtificial Neural Network
dc.subjectChlorophyll-a
dc.subjectGenetic Algorithm
dc.subjectHydrodynamics
dc.subjectTurbidity
dc.subjectWater quality
dc.typeArticle
dc.contributor.departmentTROPICAL MARINE SCIENCE INSTITUTE
dc.description.doi10.1016/j.jher.2013.04.003
dc.description.sourcetitleJournal of Hydro-Environment Research
dc.description.volume7
dc.description.issue4
dc.description.page279-299
dc.identifier.isiut000328094300008
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