Soft Sensing for Algae Blooms Based on Physical-Chemical Factors of Marine Environment


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Soft sensing can solve the problem of on-line measuring for some variables which are difficult to measure with common instruments commendably. Chlorophyll-a is an important index of water quality for seawater, which can indicate the state of algae reproduction, further more it can predict the disaster of red tide by prediction model. The content of chlorophyll-a of seawater is affected by many physical-chemical factors, this complex relationship among them is difficult to be described by ordinary mechanism expression. In this paper, we use Fuzzy BP model to describe this complex nonlinear system, and detect the content of chlorophyll-a by the method of soft sensing. The PCA(Principal Component Analysis) method had been used to reduce the dimension of the sample data, simplify the complexity of the model system, this method can make the model has a faster convergence rate and a relative low dimension. The experiment illustrates that the result of soft sensing for algae blooms can match the real changes of the content of chlorophyll-a in seawater basically.



Edited by:

Qi Luo






Y. Zhang et al., "Soft Sensing for Algae Blooms Based on Physical-Chemical Factors of Marine Environment", Applied Mechanics and Materials, Vols. 58-60, pp. 630-635, 2011

Online since:

June 2011




[1] Joseph H.W. Lee , Yan Huang , Mike Dickman , A.W. Jayawardena, Neural Network Modelling of Coastal Algal Blooms[J], Ecological Modelling, Vol. 159 (2003), pp.179-201.

DOI: 10.1016/s0304-3800(02)00281-8

[2] Assefa M. Melesse, Jayachandran Krishnaswamy and Keqi Zhang, Modeling Coastal Eutrophication at Florida Bay using Neural Networks, Journal of Coastal Research, Vol. 24 (2008), pp.190-196.

DOI: 10.2112/06-0646.1

[3] Riyad Manasrah, Mohammed Raheed, Mohammed I. Badran, Relationships between Water Temperature, Nutrients and Dissolved Oxygen in the Northern Gulf of Aqaba, Red Sea[J], Oceanologia, Vol. 48 (2006), pp.237-253.

[4] Nong J F, Huang W N, Long-term Prediction Model of BP Neural Networks Based on Principle Component Analysis, Journal of Guangxi Teachers Education University(Natural Science Edition), Vol. 25 (2008), pp.46-51.

[5] Gao W F, Yao Z H, Prediction of Algae Growth Based on BP Neural Networks, Computer, Vol. 21 (2005), pp.167-169.

[6] Wang L X, Adaptive Fuzzy Systems and Control - Design and Stability Analysis, Beijing: National Defence Industry Press, (1995).

[7] Chen Y L, Yang J H, Zhao D Z, et al. Research Progress on Red Tide Pre-warning or Predicting Models, Liaoning: Chinese Society of Marine Algae Research and Prevention of Professional Committee of the 2nd Symposium, (2007).

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