Title: Investment risk assessment based on improved BP neural network

Authors: Hongwei Liu; Xiang Li; Yiming Zhang

Addresses: School of Resources, China University of Geosciences (CUG), Hongshan District, Wuhan City, Hubei Province, China ' School of Computer Science, China University of Geosciences (CUG), Hongshan District, Wuhan City, Hubei Province, China ' School of Computer Science, China University of Geosciences (CUG), Hongshan District, Wuhan City, Hubei Province, China

Abstract: In general, the risk assessment and discussion in the actual construction process of the project mainly adopts expert scoring evaluation method, case study method, questionnaire survey method and fuzzy comprehensive evaluation method. These methods cannot provide good reference and portability for other projects. This paper proposes to use the improved BP neural network to explore the intelligent evaluation of highway investment risk. Taking the risk management data of highway engineering investment from the Second Bureau of China Communications as the research object, the differential evolution algorithm is used to improve the BP neural network model, and the existing highway investment risk data training model is used to realise the intelligent evaluation of the investment risk of new projects. The comparative experiment shows that the accuracy of the optimised evaluation model is improved by 10%, which can provide risk decision-making services for highway investment projects in China.

Keywords: BP neural network; differential evolution algorithm; risk assessment; project investment; highway.

DOI: 10.1504/IJAAC.2024.142093

International Journal of Automation and Control, 2024 Vol.18 No.6, pp.636 - 654

Received: 14 Aug 2021
Accepted: 11 Oct 2021
Published online: 07 Oct 2024 *

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