Research On Project Investment Estimation Interval Based On Support Vector Machine Regression

Investment estimation is a key link in the early stage decision making of a project,which is very important for controlling the cost of a project.It can determine whether a project succeeds or not.Engineering practice has proved that the early stage investment decision has an impact on the success of the project up to 70%.Therefore,it is very important to establish a set of scientific,accurate and effective estimation methods.The traditional investment estimation mainly relies on the personal experience of the valuer,and USES the similarity degree between the proposed project and the already built project to draw the estimated cost of the proposed project,including the production capacity index method and coefficient estimation method,etc.These estimation methods are fast but not accurate.With the progress of computer technology,more and more scholars begin to study how to use artificial intelligence estimation method to improve the accuracy and scientific nature of investment estimation.Among them,artificial neural network,support vector machine and other methods have great advantages in solving nonlinear regression problems and have been fully applied in many researches.In addition,in order to improve the estimation accuracy of artificial intelligence methods,more researchers have also introduced optimization algorithms into the estimation process,such as genetic algorithm,particle swarm optimization,differential evolution algorithm,etc.Previous studies have improved the artificial intelligence estimation system to a large extent,and made up for the defects of the traditional estimators’ estimation based on their own experience,which greatly improved the estimation accuracy.This paper mainly studies the scientificity and accuracy of the investment estimation of construction projects.Different from the existing research,this paper innovatively takes the uncertainty and risk existing in the estimation process into account,and presents it in the form of probability confidence interval.Thus,a more practical and feasible artificial intelligence estimation method is obtained to assist the early-stage investment decision and improve the decision-making efficiency.Aiming at the problem that the existing research only takes the point value when the forecast value of investment estimation is indeed fixed,and the decision risk is greatly increased due to the lack of reasonable consideration of the uncertainty of estimation,this paper introduces the grey Wolf optimization algorithm to improve the prediction accuracy of the forecast model of high point,and on this basis,proposes a probabilistic confidence interval prediction method.First,after gathering in the project case to a certain extent,this article adopts the exploratory factor analysis of SPSS statistical analysis software features a measure of the preliminary selection of 17 engineering characteristics of dimension reduction comprehensive extract public predictors,the index contains both qualitative index also includes quantitative indicators,including qualitative indicators before dimension reduction has been quantified.After exploratory factor analysis,the six common factors extracted are independent of each other and can represent most of the information of the original indicators,and they will be used as the input variables of the model.Then the regression model of SUPPORT vector machine was established according to 152 samples formed by linear interpolation in 52 actual cases,and 122 training samples and 30 test samples were randomly divided.The training model and the performance of the model were tested using the training model.During the model training,the grey Wolf optimization algorithm is used to find the optimal parameters of the SVM regression model and improve the prediction accuracy of the forecast model.Finally,according to just get a point prediction model prediction error,innovative put forward a kind of based on statistical analysis of the prediction error distribution characteristics of confidence interval estimation method,mainly by kernel density estimation method for the prediction error,the probability density function and probability distribution function and probability density function can be converted to investment estimation under different confidence level obtained and probabilistic prediction intervals.The results of multiple case studies show that the prediction accuracy of the SVM regression model is as high as 93%after the grey Wolf algorithm is optimized.When the confidence is set as 95%,the comprehensive evaluation index CWC of the interval prediction is 2.17,and the interval coverage PICP of the cost estimation is 93.33%,which indicates that both the deterministic prediction results and the probabilistic prediction results can achieve the expected effect.Therefore,this article puts forward the reliability interval prediction method is higher,the model is effective,further improve the existing system of artificial intelligence estimate for engineering construction projects of investment decision making has the realistic meaning of guidance,and provide decision makers with more abundant forecast information,reduce the uncertainty of investment decision-making,increase the ability to resist risk prediction model,it is very significant to project success.