Vulnerability Assessment Method for Immovable Cultural Relics Based on Artificial Neural Networks—An Example of a Heavy Rainfall Event in Henan Province

Can Xu; Adu Gong; Long Liang; Xiaoke Song; Yi Wang

doi:10.1007/s13753-022-00461-y

Volume 14 Issue 1

Mar. 2023

Turn off MathJax

Article Contents

Article Navigation > International Journal of Disaster Risk Science > 2023 > 14(1): 41-51

Can Xu, Adu Gong, Long Liang, Xiaoke Song, Yi Wang. Vulnerability Assessment Method for Immovable Cultural Relics Based on Artificial Neural Networks—An Example of a Heavy Rainfall Event in Henan Province[J]. International Journal of Disaster Risk Science, 2023, 14(1): 41-51. doi: 10.1007/s13753-022-00461-y

Citation:

Can Xu, Adu Gong, Long Liang, Xiaoke Song, Yi Wang. Vulnerability Assessment Method for Immovable Cultural Relics Based on Artificial Neural Networks—An Example of a Heavy Rainfall Event in Henan Province[J]. International Journal of Disaster Risk Science, 2023, 14(1): 41-51. doi: 10.1007/s13753-022-00461-y

Citation:

Can Xu, Adu Gong, Long Liang, Xiaoke Song, Yi Wang. Vulnerability Assessment Method for Immovable Cultural Relics Based on Artificial Neural Networks—An Example of a Heavy Rainfall Event in Henan Province[J]. International Journal of Disaster Risk Science, 2023, 14(1): 41-51. doi: 10.1007/s13753-022-00461-y

PDF

Vulnerability Assessment Method for Immovable Cultural Relics Based on Artificial Neural Networks—An Example of a Heavy Rainfall Event in Henan Province

doi: 10.1007/s13753-022-00461-y

Can Xu^1,2,3,
Adu Gong^{1,2,3
,
,},
Long Liang^1,2,3,
Xiaoke Song^1,2,3,
Yi Wang^1,2,3

1. State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing, 100875, China;
2. Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing, 100875, China;
3. Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China

Funds:

This research was jointly supported by the National Key Research and Development Program of China (Grant nos. 2019YFC1520801, 2019YFE01277002, and 2017YFB0504102) and the National Natural Science Foundation of China (Grant no. 41671412).

Accepted Date: 2022-11-14
Publish Date: 2022-12-22

Abstract

Abstract

Cultural relic conservation capability is an important issue in cultural relic conservation research, and it is critical to decrease the vulnerability of immovable cultural relics to rainfall hazards. Commonly used vulnerability assessment methods are subjective, are mostly applied to regional conditions, and cannot accurately assess the vulnerability of cultural relics. In addition, it is impossible to predict the future vulnerability of cultural relics. Therefore, this study proposed a machine learning-based vulnerability assessment method that not only can assess cultural relics individually but also predict the vulnerability of cultural relics under different rainfall hazard intensities. An extreme rainfall event in Henan Province in 2021 was selected as an example, with a survey report on the damage to cultural relics as a database. The results imply that the back propagation (BP) neural network-based method of assessing the vulnerability of immovable cultural relics is reliable, with an accuracy rate higher than 92%. Based on this model to predict the vulnerability of Zhengzhou City’s cultural relics, the vulnerability levels of cultural relics under different recurrence periods of heavy rainfall were obtained. Among them, the vulnerability of ancient sites is higher than those of other cultural relic types. The assessment model used in this study is suitable for predicting the vulnerability of immovable cultural relics to heavy rainfall hazards and can provide a technical means for cultural relic conservation studies.
- Artificial neural networks,
- Henan Province,
- Immovable cultural relics,
- Rainfall hazards,
- Vulnerability assessment

FullText(HTML)

References(33)

References

Chen, J.Y., K.W. Bi, and R.Z. Wen. 2009. A group vulnerability analysis method for rapid post-earthquake assessment. Earthquake Defense Technology 4(2): 174–181 (in Chinese).

Chen, J.F., Q. Li, M.H. Deng, and J.P. Pei. 2020. Urban flood vulnerability assessment based on random forest and variable fuzzy set. Yangtze River Basin Resources and Environment 29(11): 2551–2562 (in Chinese).

Goyal, D., A.K. Haritash, and S.K. Singh. 2021. A comprehensive review of groundwater vulnerability assessment using index-based, modelling, and coupling methods. Journal of Environmental Management 296: 11316

Heo, B.Y., and W.H. Heo. 2017. Economic analysis for collapse hazard areas. Applied Sciences 7(7): Article 693.

Hu, S.R. 1992. Introduction to neural networks. Beijing: National University of Defense Technology Press (in Chinese).

Hu, S.N. 1994. Neural network application technology. Beijing: National University of Defense Technology Press (in Chinese).

Huang, X.T., H.E. Li, Y. Zhang, X.F. Yang, and S.Y. Chen. 2019. Construction of urban flood vulnerability evaluation system and vulnerability assessment in Xi’an based on PSR and AHP methods. Journal of Natural Hazards 28(6): 167–175 (in Chinese).

Jiao, L.C. 1992. Theory of neural network systems. Xi’an, China: Xi’an University of Electronic Science and Technology Press (in Chinese).

Li, R. 2022. Panlu: Strengthening natural disaster risk assessment and emergency response for immovable cultural relics. China Heritage News, 2022-03-08(002). https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CCND&dbname=CCNDLAST2022&filename=CWWB202203080025&uniplatform=NZKPT&v=QXSdor4oeaRG3vkQ2ZxEhbpS20jnOwQ6TxO0yP4rniWUidBI8LATk84Akh2W2iIN5PQqIQghT3c%3d (in Chinese).

Li, Z.X., J. Du, and W. Xu. 2022. Machine learning-based vulnerability assessment of direct economic losses in rainfall-landslide hazard chains. Journal of Catastrophology 37(4): 1–7 (in Chinese).

Liang, L., A.D. Gong, Y.Z. Sun, and Y.H. Chen. 2021. Research on the risk assessment method of seasonal storm flooding of immovable cultural relics: An example of state-protected ancient sites in Fujian Province. Geomatics and Information Science of Wuhan University. https://doi.org/10.13203/j.whugis20200600 (in Chinese).

Lv, N. 2013. Conservation of cave heritage driven by the guidelines for the conservation of cultural relics in China. Ph.D thesis. Tsinghua University, Beijing, China (in Chinese).

Malmin, N.P. 2021. Historical disaster exposure and household preparedness across the United States. Disaster Medicine and Public Health Preparedness 15(1): 58–64.

Mangukiya, N.K., and A. Sharma. 2022. Flood risk mapping for the lower Narmada basin in India: A machine learning and IoT-based framework. Natural Hazards 113(2): 1285–1304.

Moghadas, M., A. Asadzadeh, A. Vafeidis, A. Fekete, and T. Kötter. 2019. A multi-criteria approach for assessing urban flood resilience in Tehran, Iran. International Journal of Disaster Risk Reduction 35: Article 101069.

Rossi, L., M. Stupazzini, D. Parisi, B. Holtschoppen, G. Ruggieri, and C. Butenweg. 2012. Empirical fragility functions and loss curves for Italian business facilities based on the 2012 Emilia-Romagna earthquake official database. Bulletin of Earthquake Engineering 18(4): 1693–1721.

Salazar, L.G.F., R. Xavier, and P. Esmeralda. 2021. Review of vulnerability indicators for fire risk assessment in cultural heritage. International Journal of Disaster Risk Reduction 60(15): Article 102286.

Seonyoung, L., and O. Seokhoon. 2022. A comprehensive seismic risk assessment map of South Korea based on seismic, geotechnical, and social vulnerability. Environmental Earth Sciences 81(1). https://doi.org/10.1007/s12665-021-10153-3.

Simonis, J.L., E.P. White, and S.K. Morgan Ernest. 2021. Evaluating probabilistic ecological forecasts. Ecology 102(8): Article e03431.

Sowell, F., and N. Sengupta. 2021. Inference for the linear IV model ridge estimator using training and test samples. Stats 4(3): 725–744.

Sun, Y.Z. 2021. Analysis of factors influencing the vulnerability of ancient sites under flooding. China Cultural Heritage No. 4): 19–23 (in Chinese).

Taromideh, F., R. Fazloula, B. Choubin, A. Emadi, and R. Berndtsson. 2022. Urban flood-risk assessment: Integration of decision-making and machine learning. Sustainability 14(8): Article 4483.

Wang, L.H., H.J. Huang, S.H. Cui, and X.J. Wan. 2022. Analysis of the spatial and temporal dynamics of the “7.20” flood disaster in Henan Province. Journal of Catastrophology 37(3): 205–211 (in Chinese).

Wang, Z.N., P.N. Sharma, and J.W. Cao. 2016. From knowledge sharing to firm performance: A predictive model comparison. Journal of Business Research 69(10): 4650–4658.

Wei, X.J., X.M. Zhao, and M.M. Xiao. 2018. Research on the influence factors of rainfall-based landslide disaster based on SVM. China Computer and Communication 16: 32–33 (in Chinese).

Wen, Y.J., and H.W. Yang. 2016. Macro-population vulnerability assessment of earthquake hazards in Shaanxi Province. South China Earthquake 36(4): 42–49 (in Chinese).

Wen, Q.P., Y.H. Zhou, Z.G. Huo, L. Li, S.D. Fang, R.Q. Shi, and Q. Che. 2018. A quantitative study on vulnerability assessment of torrential rainfall and flooding in Hubei. China Agricultural Meteorology 39(8): 547–557 (in Chinese).

Yacine, A., S. Zahra, T. Rania, P.Q. Bao, P.S. Chandra, M. Firuza, and B.S. Fusun. 2021. Assessing landslide susceptibility using a machine learning-based approach to achieving land degradation neutrality. Environmental Earth Sciences 80(17). https://doi.org/10.1007/s12665-021-09889-9.

Zhang, K.Z., J.Q. Shen, G. Li, E.W. Boyer, C.R. Mello, L. Ping, L. Hu, J.H. Gao, and B.H. Fan. 2021. Flood drainage rights in watersheds based on the harmonious allocation method. Journal of Hydrology 601: Article 126627.

Zhang, X.R., Y. Wang, T.X. Liu, and Y. Chen. 2021. Research progress of building vulnerability curve associated with mountain rorrent. Mountain Research 39(3): 356–366 (in Chinese).

Zhang, L.X., X.J. Yang, J. Chen, Z. Wang, J. Zhang, and J. Yu. 2015. Vulnerability assessment and mechanism of human-land system in the Han Dynasty Chang’an large relic area. Resource Science 37(9): 1848–1859 (in Chinese).

Zhao, X.L., X. Wang, Z.X. Zhang, L. Yi, B. Liu, and J.Y. Xu. 2010. Spatiotemporal characteristics of land cover change in Henan Province. Bulletin of Soil and Water Conservation 30(2): 24–30 (in Chinese).

Zhu, Y.X. 2017. The problems and countermeasures in the practice of constructing “cultural highlands” –The perspective of constructing a national important “cultural highland” in Henan. Tribune of Study 33(11): 61–66 (in Chinese).

Relative Articles

Supplements(0)

Cited By

Proportional views