Subway Station Investment Prediction Based on IGWO-SVR
To predict the investment of subway stations quickly and accurately in the feasibility study stage and provide decision support for investors,this paper proposed a method to optimize the parameters of support vector regression ma-chine based on improved grey wolf optimizer.Firstly,by collecting and sorting out cases of subway stations and estab-lishing a database as the original prediction sample,bootstrapping was adopted to expand the sample to solve the prob-lem of low accuracy of small sample prediction.Secondly,the initial population generation,convergence factor and lo-cation update of the grey wolf optimizer were improved to avoid the problem of grey wolf optimizer being stuck in local optima.Subsequently,the improved grey wolf optimizer was used to optimize the parameters of support vector regres-sion machine to establish a subway station investment prediction model.Finally,a station example was used to verify the validity of the prediction model.The results show that the average relative error of the prediction model of the sup-port vector regression machine optimized by the improved grey wolf algorithm is 4.33％,with the goodness of fit of 0.944 0 on the test set.The prediction model based on improved grey wolf algorithm with optimized parameters is bet-ter than the unoptimized,particle swarm optimized and grey wolf algorithm optimized support vector regression predic-tion models.The relative error of the case of Honggaolu station is 4.87％,proving the effectiveness of the proposed prediction method.
investment predictionbootstrapgrey wolf optimizersupport vector regressionsubway station
万方数据
维普
