Research On The Air Quality Prediction Of Underground Construction Based On PSO-SVM Model

With the haze problem continued to outburst in domestic city,the impact of air pollution issue for the human health is obtaining more and more attention;nowadays,having a coordinated development for the environment in the modern urban construction becomes one of the central work for the government,as well as the focused issue for the society.It is necessary to study the air pollution issue scientifically before regulating the problem,from which the core index parameter for affecting air quality could be recognized,then the reasonable regulation for improving the air quality would be obtained.At present,there is not an uniform framework for establishing an evaluation model for the air quality,which is usually affected by the subjective factors of the researchers and will be resulting that the non-linear relation between the air pollutant concentration and the air quality grade is hard determined,then the corresponding air quality forecast has large problems with low accuracy and efficiency.The support vector machine method is a modern machine learning technique which is firstly proposed in last century and has a rapid development and improvement then,it is based on the statistical learning theory in principle and followed with the minimization principle of the structural risk of the system.At present,the support vector machine method has been widely applied in various nonlinear problems of many fields,to avoid the “curse of dimensionality”,a nonlinear kernel function is introduced into the support vector machine method so that the imposed variables from the low dimensional feature space can be mapped into a high dimensional feature space,thus the nonlinear problem is turned into solving linear equations;the support vector machine method has a very outstanding generalization ability than other traditional statistical method,therefore it has been widely used in the areas such as finance,biological information identification,architecture science nowadays.In this paper,the traditional support vector machine method is combined with the particle swarm optimization algorithm,thus a support vector machine monitoring and pre-warning model based on the particle swarm optimization algorithm is established,which is simply short for PSO-SVM model;through the parameter optimization of the particle swarm optimization,the precision of the core parameter of SVM model can be effectively ensured in theory,which will largely shorten the consuming time and effectively improve the precision of whole monitoring and pre-warning model;To verify the actual pre-warning precision of the PSO-SVM model mentioned in this paper,regressive prediction of the air qualify grade for the typical underground construction,which is the underground station in the Hankou railway station of Wuhan city,is researched respectively with the traditional SVM model,the SVM model basing on the genetic algorithm and the PSO-SVM model,by comparing with the corresponding results,the most effective and highest precision model for predicting air quality grade of the underground construction is obtained.The support vector machine method is studied and combined with the focus issue of human livelihood in this paper,through which the accuracy of the PSO-SVM monitoring and pre-warning model is effectively verified,and this paper will have a significant theoretical guidance meaning for the real-time regulation of the air quality of the underground construction in the future.