Optimization Algorithm Based On Support Vector Machine And Its Application In Water Quality Predictio

The prediction of water quality is very important issue,which can carry out planning and management of water resources,water pollution treatment is the basic work,and therefore it is of great significance.It is not easy to use traditional methods building an accurate nonlinear predictive model.So,scholars have achieved some results with neural networks,time series,regression analysis,gray theory,fuzzy reasoning and so on.These methods are able to predict water quality in some extent,but they are also significant drawbacks.Because artificial neural networks has good features nonlinear mapping and self-learning ability,it is suitable for describing trends complex water environment,the prediction of water quality has become a hot topic in today’s field of prediction,but the neural network is slow in convergence and easy to fall into local extremes defects,and it seriously affect the quality prediction accuracy.This has forced scholars continue to study and improve some other intelligent algorithms to solve the neural network forecasting method in practical prediction and the prediction effect instability.In this paper,support vector regression-based approach,which not only has excellent generalization ability,but also change it ultimately into a convex quadratic programming problem,you can get the global optimal solution in theory,so as to solve the traditional neural network question that can not be avoided local optimization problem;but the standard support vector regression model results are sometimes not ideal,this is due to the support vector regression algorithm,variance of random error term monitoring data are not caused by the same punishment scale,but the error term is the same punishment scale,the same punishment scale often leads to the variance of the regression line drawn large items,so compared to the large variance degree term fitting fit of items variance difference,the error is large,in order to solve the same punishment Methods scale this problem,we used in the standard support vector regression model by adding the weights to adjust the degree of regression in the regression,which is controlled by the weights affect heteroscedasticity.The algorithm although improves the prediction accuracy in some extent,but the result is still not ideal.Based on these studies,the PSO algorithm is applied to the weighted support vector regression,and support vector regression weighted combination of parameters are optimized,and the predicted target Luoshi Dali,predict its water quality parameters.Respectively,BP neural network,RBF neural networks and weighted SVR comparison.Simulation results show that the particle swarm optimization based on weighted support vector regression applied to Dali Luoshi prediction of water quality,the water quality prediction model set up research methods superior to other methods,the prediction accuracy is improved.