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Application of BP Neural Network Model in Risk Evaluation of Railway Construction

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Listed:
  • Yang Changwei
  • Li Zonghao
  • Guo Xueyan
  • Yu Wenying
  • Jin Jing
  • Zhu Liang

Abstract

Chinese railway construction project is an important part of the implementation of the “Belt and Road” strategy, and the risk evaluation of overseas railway construction is the primary link of the project. Firstly, this paper mainly analyzes the Asian and European countries along the railway construction project, establishes a railway construction project risk evaluation system, and synthesizes various risk factors. Secondly, it establishes two independent BP neural network models by using different training algorithms because of the different political, economic, and cultural elements between the two continents.

Suggested Citation

  • Yang Changwei & Li Zonghao & Guo Xueyan & Yu Wenying & Jin Jing & Zhu Liang, 2019. "Application of BP Neural Network Model in Risk Evaluation of Railway Construction," Complexity, Hindawi, vol. 2019, pages 1-12, June.
    • Handle: RePEc:hin:complx:2946158
    DOI: 10.1155/2019/2946158
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    References listed on IDEAS

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    1. Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
    2. Anastas Vangeli, 2017. "China's Engagement with the Sixteen Countries of Central, East and Southeast Europe under the Belt and Road Initiative," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 25(5), pages 101-124, September.
    3. Badiru, Adedeji B. & Sieger, David B., 1998. "Neural network as a simulation metamodel in economic analysis of risky projects," European Journal of Operational Research, Elsevier, vol. 105(1), pages 130-142, February.
    4. Lu Zhang & Hongru Du & Yannan Zhao & Rongwei Wu & Xiaolei Zhang, 2017. "Urban networks among Chinese cities along "the Belt and Road": A case of web search activity in cyberspace," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-20, December.
    5. Enrico Fardella & Giorgio Prodi, 2017. "The Belt and Road Initiative Impact on Europe: An Italian Perspective," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 25(5), pages 125-138, September.
    6. Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
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