Abstract
The investment risk assessment remains a challenge for public-private partnership (PPP) projects under potential risks and complex uncertainties. To address this problem, a least squares support vector machine (LSSVM) based quantum-behaved particle swarm optimization (QPSO) method is proposed to evaluate the investment risk assessment for PPP projects in this paper. This method uses quantum theory to observe the particle state optimization to improve the accuracy of assessment results. Applying this method to the investment risk assessment of these PPP projects with 40 PPP projects in Hubei and Zhejiang Province in China. The results show that the maximum relative error and average relative error of this risk assessment are smaller than the traditional PSO-SVM and backpropagation neural network method. Compared with the existing methods of investment risk assessment, this method improves the accuracy and efficiency of risk assessment of PPP projects.
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