Paper Info
Title
A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction.
Abstract
Water quality prediction plays an important role in modern intensive river crab aquaculture management. Due to the nonlinearity and non-stationarity of water quality indicator series, the accuracy of the commonly used conventional methods, including regression analyses and neural networks, has been limited. A prediction model based on support vector regression (SVR) is proposed in this paper to solve the aquaculture water quality prediction problem. To build an effective SVR model, the SVR parameters must be set carefully. This study presents a hybrid approach, known as real-value genetic algorithm support vector regression (RGA–SVR), which searches for the optimal SVR parameters using real-value genetic algorithms, and then adopts the optimal parameters to construct the SVR models. The approach is applied to predict the aquaculture water quality data collected from the aquatic factories of YiXing, in China. The experimental results demonstrate that RGA–SVR outperforms the traditional SVR and back-propagation (BP) neural network models based on the root mean square error (RMSE) and mean absolute percentage error (MAPE). This RGA–SVR model is proven to be an effective approach to predict aquaculture water quality.
Year
DOI
Venue
2013
10.1016/j.mcm.2011.11.021
Mathematical and Computer Modelling
Keywords
Field
DocType
Water quality prediction,Support vector regression,Genetic algorithms
Mean absolute percentage error,Data mining,Aquaculture,Regression,Support vector machine,Mean squared error,Artificial intelligence,Artificial neural network,Machine learning,Genetic algorithm,Water quality,Mathematics
Journal
Volume
Issue
ISSN
58
3
0895-7177
Citations 
PageRank 
References 
12
0.63
9
Authors
6
Name
Order
Citations
PageRank
Shuangyin Liu1305.89
Haijiang Tai2173.28
Qisheng Ding3213.81
Daoliang Li433481.09
Longqin Xu5344.64
Yaoguang Wei615816.44