Prediction of water quality time series data based on least squares support vector machine

Actual water quality monitoring sites due to inadequate and abnormal by the impact to water quality warning has brought new challenges. How do based on limited monitoring data to predict the water quality to address the shortage of river water quality monitoring sites and data coverage of false alarms caused by abnormal, early warning of water pollution are of great significance. According to river water monitoring data, the small sample properties, is proposed based on least squares support vector machine prediction of water quality parameters, this method has strong ability to predict the true value, and the global optimization and good generalization. This method is applied in the river water quality measurement data, after training the LS-SVM model for water quality parameters of water quality monitoring system to predict, in the same sample under the BP network and RBF network prediction. Experimental results show that the small sample case with noise, least squares support vector machine method is better than multi-layer BP and RBF neural network, to better meets the requirements of water quality prediction. Comparing data of the two experiments shows that new module makes the interest mining more effective. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Kunming University of Science and Technology