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.