Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.marpolbul.2008.05.021
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dc.titleAn ANN application for water quality forecasting
dc.contributor.authorPalani, S.
dc.contributor.authorLiong, S.-Y.
dc.contributor.authorTkalich, P.
dc.date.accessioned2014-12-12T07:29:38Z
dc.date.available2014-12-12T07:29:38Z
dc.date.issued2008-09
dc.identifier.citationPalani, S., Liong, S.-Y., Tkalich, P. (2008-09). An ANN application for water quality forecasting. Marine Pollution Bulletin 56 (9) : 1586-1597. ScholarBank@NUS Repository. https://doi.org/10.1016/j.marpolbul.2008.05.021
dc.identifier.issn0025326X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/115579
dc.description.abstractRapid urban and coastal developments often witness deterioration of regional seawater quality. As part of the management process, it is important to assess the baseline characteristics of the marine environment so that sustainable development can be pursued. In this study, artificial neural networks (ANNs) were used to predict and forecast quantitative characteristics of water bodies. The true power and advantage of this method lie in its ability to (1) represent both linear and non-linear relationships and (2) learn these relationships directly from the data being modeled. The study focuses on Singapore coastal waters. The ANN model is built for quick assessment and forecasting of selected water quality variables at any location in the domain of interest. Respective variables measured at other locations serve as the input parameters. The variables of interest are salinity, temperature, dissolved oxygen, and chlorophyll-a. A time lag up to 2Δt appeared to suffice to yield good simulation results. To validate the performance of the trained ANN, it was applied to an unseen data set from a station in the region. The results show the ANN's great potential to simulate water quality variables. Simulation accuracy, measured in the Nash-Sutcliffe coefficient of efficiency (R2), ranged from 0.8 to 0.9 for the training and overfitting test data. Thus, a trained ANN model may potentially provide simulated values for desired locations at which measured data are unavailable yet required for water quality models. © 2008 Elsevier Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.marpolbul.2008.05.021
dc.sourceScopus
dc.subjectData driven technique
dc.subjectForecasting
dc.subjectNeural network
dc.subjectPrediction
dc.subjectSingapore seawater
dc.subjectSoutheast Asia
dc.subjectWater quality
dc.typeArticle
dc.contributor.departmentTROPICAL MARINE SCIENCE INSTITUTE
dc.description.doi10.1016/j.marpolbul.2008.05.021
dc.description.sourcetitleMarine Pollution Bulletin
dc.description.volume56
dc.description.issue9
dc.description.page1586-1597
dc.description.codenMPNBA
dc.identifier.isiut000259768100020
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