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  • ISSN 1007-6336
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Volume 34 Issue 1
Jan.  2015
Article Contents

Citation:

Prediction method of Chlorophyll-a concentration in seawater based on extreme learning machine regression

  • Received Date: 2014-01-04
    Accepted Date: 2014-03-05
  • Effective monitoring the state of chlorophyll-a concentration in seawater plays an important role for the early warning of marine disasters, such as coastal red tides. Grey correlation analysis method is used to determine the input variables of the prediction model. It can effectively reduce the dimension of the model system. Extreme learning machine regression (ELMR) method was used to build the prediction model of chlorophyll-a concentration in seawater. Comparing with the generalized regression neural network and support vector machine regression model, it indicates that extreme learning machine regression has better accuracy, efficiency and generalization ability of prediction than other methods. It adapts to be used for predicting the concentration of chlorophyll-a in this researched seawater area.
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  • [1] 王 千,李 哲,范 洁.我国近岸海洋生态环境监测研究进展 [J].国土资源情报,2013,3(1):44-48.[8]崔东文.极限学习机在湖库总磷、总氮浓度预测中的应用 [J].水资源保护,2013,3(29): 61-66.
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Prediction method of Chlorophyll-a concentration in seawater based on extreme learning machine regression

  • 1. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China

Abstract: Effective monitoring the state of chlorophyll-a concentration in seawater plays an important role for the early warning of marine disasters, such as coastal red tides. Grey correlation analysis method is used to determine the input variables of the prediction model. It can effectively reduce the dimension of the model system. Extreme learning machine regression (ELMR) method was used to build the prediction model of chlorophyll-a concentration in seawater. Comparing with the generalized regression neural network and support vector machine regression model, it indicates that extreme learning machine regression has better accuracy, efficiency and generalization ability of prediction than other methods. It adapts to be used for predicting the concentration of chlorophyll-a in this researched seawater area.

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