NDLI: Chlorophyll-a predicting based on artificial neural network for marine cage fish farming area in dapeng cove in Daya Bay, South China Sea

Content Provider	IEEE Xplore Digital Library
Author	Xiuli Liao Honghui Huang Ming Dai Zhanhui Qi
Copyright Year	2012
Description	Author affiliation: South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou, China (Xiuli Liao; Honghui Huang; Ming Dai; Zhanhui Qi)
Abstract	Base on the 201 groups of data that accepted in the last ten years, a 3 layer (3,8,1) BP artificial neural network model on quickly predicting chlorophyll-a concentration in marine cage fish farming area was established. The model was established in software MATLAB7.1 (MATTrix LABoratory) using BP network. Three field accurate measurement parameters (water temperature, pH, dissolved oxygen) was as the input variable and chlorophyll-a was the output in our model. In most condition the forecast results was closely to the actual data when using this model. Its prediction accuracy was significantly higher than the linear regression equation. For the reason that the data used in building model which has some question and the complexity of predicting chlorophyll-a content, there existed some error between forecast value and actual value when using this model in several sets of data. This article put forward the methods to consummate the model in the next step.
Starting Page	203
Ending Page	206
File Size	388167
Page Count	4
File Format	PDF
ISBN	9781457721304
ISSN	21579563
e-ISBN	9781457721335
DOI	10.1109/ICNC.2012.6234720
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	chlorophyll-a Biological system modeling BP artificial neural network Linear regression marine cage fish farming area Artificial neural networks Predictive models predict Data models Mathematical model Tides
Content Type	Text
Resource Type	Article

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