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Efficient Risk Assessment of Landslide Disasters in Changsha City Based on Machine Learning
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Can WANG1, 2, Hao XIAO3, 4, 5, Ting XIAO3, 4, 5, Yaqi FANG1, 2, Leilei LIU3, 4, 5
Mining and Metallurgical Engineering | 2023, 43(5) : 26 - 31
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Mining and Metallurgical Engineering | 2023, 43(5): 26-31
• MINING •
Efficient Risk Assessment of Landslide Disasters in Changsha City Based on Machine Learning
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Can WANG1, 2, Hao XIAO3, 4, 5, Ting XIAO3, 4, 5, Yaqi FANG1, 2, Leilei LIU3, 4, 5
Affiliations
  • 1.Hunan Institute of Geological Disaster Investigation and Monitoring, Changsha 410004, Hunan, China
  • 2.Hunan Geological Disaster Monitoring, Early Warning and Emergency Rescue Engineering Technology Research Center, Changsha 410004, Hunan, China
  • 3.Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha 410083, Hunan, China
  • 4.Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Changsha 410083, Hunan, China
  • 5.School of Geosciences and Info-Physics, Central South University, Changsha 410083, Hunan, China
Published: 2023-10-01 doi: 10.3969/j.issn.0253-6099.2023.05.006
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Random Forest (RF) and eXtreme Gradient Boosting model (XGboost) were adopted to assess the landslide hazard in Changsha City, and frequency ratio (FR) was then used to check and verify the obtained result. A vulnerability assessment system was established based on analytic hierarchy process (AHP) and then adopted to make vulnerability assessment. Finally, efficient risk assessment was realized by integrating the results of hazard assessment and vulnerability assessment with numerical classification method. It is found that RF model is similar to XGBoost model in its evaluation performance, but XGBoost model regulated by FR method can bring a more accurate assessment. Also, the vulnerability assessment has the largest weight value of population density, and high vulnerability areas are mostly concentrated in downtown area and traffic arteries. The areas with relatively higher and high risk account for about 4.6% of the entire study area in Changsha City, which are mainly concentrated in valleys, towns and traffic arteries.

landslide hazard  /  machine learning  /  landslide  /  random forests (RF)  /  eXtreme Gradient Boosting (XGBoost)  /  hazard assessment  /  risk assessment
Can WANG, Hao XIAO, Ting XIAO, Yaqi FANG, Leilei LIU. Efficient Risk Assessment of Landslide Disasters in Changsha City Based on Machine Learning[J]. Mining and Metallurgical Engineering, 2023 , 43 (5) : 26 -31 . DOI: 10.3969/j.issn.0253-6099.2023.05.006
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Article Info
doi: 10.3969/j.issn.0253-6099.2023.05.006
  • Receive Date：2023-04-23
  • Online Date：2026-03-05
  • Published：2023-10-01
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History
  • Received：2023-04-23
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Affiliations
    1.Hunan Institute of Geological Disaster Investigation and Monitoring, Changsha 410004, Hunan, China
    2.Hunan Geological Disaster Monitoring, Early Warning and Emergency Rescue Engineering Technology Research Center, Changsha 410004, Hunan, China
    3.Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha 410083, Hunan, China
    4.Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Changsha 410083, Hunan, China
    5.School of Geosciences and Info-Physics, Central South University, Changsha 410083, Hunan, China
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