Cultural Heritage Risk Assessment Based on Explainable Machine Learning Models: A Case Study of the Ancient Tea Horse Road in China
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 cult...
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MDPI AG
2025-03-01
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| author | Hao Zhang Bo Shu Yang Liu Yang Wei Huizhen Zhang |
| author_facet | Hao Zhang Bo Shu Yang Liu Yang Wei Huizhen Zhang |
| author_sort | Hao Zhang |
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| description | 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. |
| format | Article |
| id | doaj-art-9cdedb55915c44848ea7d07150dad0ef |
| institution | OA Journals |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-03-01 |
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| spelling | doaj-art-9cdedb55915c44848ea7d07150dad0ef2025-08-20T02:18:14ZengMDPI AGLand2073-445X2025-03-0114473410.3390/land14040734Cultural Heritage Risk Assessment Based on Explainable Machine Learning Models: A Case Study of the Ancient Tea Horse Road in ChinaHao Zhang0Bo Shu1Yang Liu2Yang Wei3Huizhen Zhang4School of Architecture, Southwest Jiaotong University, Chengdu 610065, ChinaSchool of Design, Southwest Jiaotong University, Chengdu 610065, ChinaSchool of Architecture, Southwest Jiaotong University, Chengdu 610065, ChinaSchool of Design, Southwest Jiaotong University, Chengdu 610065, ChinaSchool of Architecture, Southwest Jiaotong University, Chengdu 610065, ChinaAs 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.https://www.mdpi.com/2073-445X/14/4/734cultural heritageLightGBMSHAPrisk maps |
| spellingShingle | Hao Zhang Bo Shu Yang Liu Yang Wei Huizhen Zhang Cultural Heritage Risk Assessment Based on Explainable Machine Learning Models: A Case Study of the Ancient Tea Horse Road in China Land cultural heritage LightGBM SHAP risk maps |
| title | Cultural Heritage Risk Assessment Based on Explainable Machine Learning Models: A Case Study of the Ancient Tea Horse Road in China |
| title_full | Cultural Heritage Risk Assessment Based on Explainable Machine Learning Models: A Case Study of the Ancient Tea Horse Road in China |
| title_fullStr | Cultural Heritage Risk Assessment Based on Explainable Machine Learning Models: A Case Study of the Ancient Tea Horse Road in China |
| title_full_unstemmed | Cultural Heritage Risk Assessment Based on Explainable Machine Learning Models: A Case Study of the Ancient Tea Horse Road in China |
| title_short | Cultural Heritage Risk Assessment Based on Explainable Machine Learning Models: A Case Study of the Ancient Tea Horse Road in China |
| title_sort | cultural heritage risk assessment based on explainable machine learning models a case study of the ancient tea horse road in china |
| topic | cultural heritage LightGBM SHAP risk maps |
| url | https://www.mdpi.com/2073-445X/14/4/734 |
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