Research on the initial scale of the Belt and Road International Earthquake Cooperation Fund based on VaR method of BP neural network model of big data

Guo-qu Deng; Qian Han; Can Li; Hu Chen

doi:10.1117/12.2628432

7 March 2022 Research on the initial scale of the Belt and Road International Earthquake Cooperation Fund based on VaR method of BP neural network model of big data

Guo-qu Deng, Qian Han, Can Li, Hu Chen

Author Affiliations +

Proceedings Volume 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021); 121671E (2022) https://doi.org/10.1117/12.2628432
Event: 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, 2021, Harbin, China

Abstract

The area along The Belt and Road is located in the Mediterranean Sea-the Himalaya Seismic Belt and the Pacific Rim Seismic Belt, which is one of the regions with the most frequent earthquake disasters and the most severe losses in the world. The establishment of The Belt and Road earthquake international cooperation fund can help maintain regional society peace, stability and prosperity. Based on The Belt and Road earthquake data of EM-DAT，this paper uses the neural network model VaR method to calculate the fund size of The Belt and Road earthquake international cooperation fund under different risk tolerances. According to the research results, when the risk tolerance is 90%, 95%, 99%, and 99.5%, the initial fund sizes are predicted to be 250.8 million U.S. dollars, 444.8 million U.S. dollars, 633.2 million U.S. dollars, and 910.8 million U.S. dollars, respectively.

Citation Download Citation

Guo-qu Deng, Qian Han, Can Li, and Hu Chen "Research on the initial scale of the Belt and Road International Earthquake Cooperation Fund based on VaR method of BP neural network model of big data", Proc. SPIE 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), 121671E (7 March 2022); https://doi.org/10.1117/12.2628432

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KEYWORDS

Earthquakes

Roads

Data modeling

Neural networks

Databases

Neurons

Tolerancing

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