Design and implementation of construction cost prediction model based on SVM and LSSVM in industries 4.0

Abstract

Purpose In order to improve the accuracy of project cost prediction, considering the limitations of existing models, the construction cost prediction model based on SVM (Standard Support Vector Machine) and LSSVM (Least Squares Support Vector Machine) is put forward. Design/methodology/approach In the competitive growth and industries 4.0, the prediction in the cost plays a key role. Findings At the same time, the original data is dimensionality reduced. The processed data are imported into the SVM and LSSVM models for training and prediction respectively, and the prediction results are compared and analyzed and a more reasonable prediction model is selected. Originality/value The prediction result is further optimized by parameter optimization. The relative error of the prediction model is within 7%, and the prediction accuracy is high and the result is stable.