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

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.