Cultural Heritage Risk Assessment Based on Explainable Machine Learning Models: A Case Study of the Ancient Tea Horse Road in China

Abstract

As the core carrier of historical and cultural identity, cultural heritage is facing multiple threats such as natural disasters, human activities and its own vulnerability. There is an increasing number of studies on cultural heritage risk assessment around the world, but the risk assessment of cultural heritage in China has not been fully explored. In this paper, the LightGBM model was used to quantitatively analyze the main influencing factors of cultural heritage risk along the Ancient Tea Horse Road in Sichuan, and spatial analysis was carried out by combining geographic information system (GIS) technology. In order to improve the interpretability of the assessment results, the SHAP method was introduced to systematically evaluate the contribution of each influencing factor to the risk of cultural heritage. This study identified seven major risk factors, including landslides, collapses, debris flows, earthquakes, soil erosion, urban road networks, and cultural heritage vulnerability, and constructed a risk assessment framework that comprehensively considers the vulnerability to natural and synthetic factors and the heritage itself. The results of the assessment divided the risk of cultural heritage sites into five levels: very low, low, medium, high and very high, and the results showed that 52.36% of the cultural heritage was classified as at medium and high risk and above, revealing the severe security situation faced by cultural heritage in the region and indicating the urgent need to take effective protective and management measures to deal with multiple risks and challenges.

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