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Water quality prediction is an important and widely studied topic since it has significant impact on national or regional ecological and water resources management. Due to water quality indicators series nonlinearity and non-stationary, the accuracy of conventional mostly used methods including regression analysis, ARIMA and neural network has been limited. The use of support vector machine has been...
A predictive model of water-quality, which based on wavelet transform and support vector machine, is proposed. This model uses wavelet transform to get water time sequence variations in different scale, and optimizes three parameters of Regression Support Vector Machine with improved Particle Swarm Optimization algorithm, to improve the accuracy of prediction model. This model is used to take one-step...
In view of the deficiency of the traditional methods, according to the analysis of surface water in Suzhou city, a BP neural network model is proposed to evaluate water quality. Firstly The present situation and changing trends of surface water are analyzed. The structure of BP model is described and the choice of hidden layer is also optimized. Finally, the proposed model was applied to evaluate...
This paper deals with the study of a water quality prediction model through application of LS-SVM in Liuxi River in Guangzhou. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value, least squares support vector machine (LS-SVM) combined with particle swarm optimization (PSO) is used to time series prediction. The LS-SVM can overcome...
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