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Logistic Regression Prediction Model for Rainfall-Induced Landslide Disasters in Yuanma Basin
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Min ZHANG1, Hongxia QU2, Jianqing HE3, Jin WANG3
Mining and Metallurgical Engineering | 2025, 45(6) : 33 - 38
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Mining and Metallurgical Engineering | 2025, 45(6): 33-38
• MINING •
Logistic Regression Prediction Model for Rainfall-Induced Landslide Disasters in Yuanma Basin
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Min ZHANG1, Hongxia QU2, Jianqing HE3, Jin WANG3
Affiliations
  • 1.Hainan Vocational University, Haikou 570216, Hainan, China
  • 2.Space Information Co., Ltd., Zhejiang Natural Resources Group, Jinhua 312000, Zhejiang, China
  • 3.Hunan University of Science and Technology, Xiangtan 411201, Hunan, China
doi: 10.3969/j.issn.0253-6099.2025.06.006
Outline
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The typical region of the Yuanma Basin (covering Mayang, Luxi and Yuanling counties) was taken in the research of landslide disaster. Based on the database of detailed survey of geological disasters from 2013 to 2022, landslide events with complete rainfall records were selected for research. Then, significant rainfall factors for landslide, including rainfall on the day of landslide occurrence and during those four days prior to it, and maximum rainfall rate on the occurrence day were selected out by adopting logistic regression analysis, and a logistic regression prediction model for rainfall-induced landslides in the Yuanma Basin was developed and validated by studying representative landslide cases. The research results indicate that the rainfall-induced landslides in the Yuanma Basin is significantly correlated with rainfall on the occurrence day and during those 4 days prior to the occurrence. This confirms that the period including the occurrence day and those 4 days prior to the occurrence is critical for early warning of landslide in this region. A combined rainfall factors, consisting of R0 (daily rainfall on the occurrence day), R1 (rainfall of 1 day before occurrence), R2 (rainfall of 2 days before occurrence) and Rh (maximum rainfall rate on the occurrence day), present optimal significance in statistical analysis. The practical cases have validated that this logistic regression model constructed based on this factor combination has a prediction accuracy of 83%. It is concluded that this model can give a prediction with high precision and is of practical value.

Yuanma Basin  /  geological disaster  /  landslide  /  rainfall threshold  /  logistic regression  /  early warning of landslide  /  prediction model
张敏, 渠红霞, 贺建清, 王津. 沅麻盆地降雨型滑坡灾害逻辑回归预测模型. 矿冶工程杂志, 2025 , 45 (6) : 33 -38 . DOI: 10.3969/j.issn.0253-6099.2025.06.006
Min ZHANG, Hongxia QU, Jianqing HE, Jin WANG. Logistic Regression Prediction Model for Rainfall-Induced Landslide Disasters in Yuanma Basin[J]. Mining and Metallurgical Engineering, 2025 , 45 (6) : 33 -38 . DOI: 10.3969/j.issn.0253-6099.2025.06.006
Year 2025 volume 45 Issue 6
103
14
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doi: 10.3969/j.issn.0253-6099.2025.06.006
  • Receive Date:2025-06-30
  • Online Date:2026-10-08
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  • Received:2025-06-30
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Affiliations
    1.Hainan Vocational University, Haikou 570216, Hainan, China
    2.Space Information Co., Ltd., Zhejiang Natural Resources Group, Jinhua 312000, Zhejiang, China
    3.Hunan University of Science and Technology, Xiangtan 411201, Hunan, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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