Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jher.2013.04.003
Title: Hybrid ANN-GA model for predicting turbidity and chlorophyll-a concentrations
Authors: Mulia, I.E. 
Tay, H. 
Roopsekhar, K. 
Tkalich, P. 
Keywords: Artificial Neural Network
Chlorophyll-a
Genetic Algorithm
Hydrodynamics
Turbidity
Water quality
Issue Date: Dec-2013
Citation: Mulia, 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
Abstract: The 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.
Source Title: Journal of Hydro-Environment Research
URI: http://scholarbank.nus.edu.sg/handle/10635/110872
ISSN: 15706443
DOI: 10.1016/j.jher.2013.04.003
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