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A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction

  • Published:2013-08 Issue:3-4 Volume:58 Page:458-465
  • ISSN:0895-7177
  • Container-title:Mathematical and Computer Modelling
  • language:en
  • Short-container-title:Mathematical and Computer Modelling

Author:

Liu Shuangyin,Tai Haijiang,Ding Qisheng,Li Daoliang,Xu Longqin,Wei Yaoguang

Funder

National Key Technology R&D Program

Beijing Natural Science Foundation

National Major Science and Technology Project of China

Project of Ministry of Agriculture of the People’s Republic of China

Publisher

Elsevier BV

Subject

Computer Science Applications,Modelling and Simulation

Reference29 articles.

1. Greedy regression ensemble selection: theory and an application to water quality prediction;Partalas;Inform Sciences,2008

2. Dissolved oxygen prediction in apostichopus japonicas aquaculture ponds by BP neural network and AR model;Li;Sensor Letters,2010

3. A hydrodynamic and water quality modeling study of spatial and temporal patterns of phytoplankton growth in a stratified lake with buoyant incoming flow

4. An efficient self-organizing RBF neural network for water quality prediction;Han;Neural Networks,2011

5. Development of a neural network for dissolved oxygen in seawater;Palani;Indian Journal of Marine Sciences.,2009
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