Regional landslide hazard assessment using the IV-RF coupling model and critical monthly average rainfall threshold：A case study from Fuling District, Chongqing
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摘要:
提高降雨型滑坡易发性预测精度和构建合适的降雨阈值模型对区域滑坡危险性评价具有重要意义。以重庆市涪陵区为例，采用信息量模型、BP神经网络模型、随机森林模型、信息量-BP神经网络耦合模型和信息量-随机森林耦合模型进行区域滑坡易发性评价，对比不同模型下的接受者操作特征曲线、曲线下方面积和易发性分布规律。提出滑坡临界月平均降雨阈值模型，反演出不同时间概率下的临界月平均降雨阈值。将易发性结果与时间概率等级进行耦合得到区域滑坡危险性评价结果并随机选取30次滑坡事件与4次典型滑坡事件进一步验证了评价精度。研究结果表明：信息量和机器学习模型进行耦合，弥补了机器学习在前期数据输入和非样本选择的缺点，提升了单一机器学习模型的预测精度，其中信息量-随机森林耦合模型预测精度最高；随机选取的30例滑坡样本中，有20例滑坡（占67%）位于发生时间概率50%以上区域，验证了临界月平均降雨阈值模型的精度；随机选取的4例典型滑坡样本中，时间概率等级基本为P4或P5，且位置均位于高危险区与极高危险区中，与现场调查结果基本一致，说明基于信息量-随机森林耦合模型和临界月平均降雨阈值的区域滑坡危险性评价结果准确且可靠。
Abstract:Improving the accuracy of susceptibility prediction for rainfall-induced landslides and establishing suitable rainfall threshold models are of great significance for regional landslide hazard assessment. Taking Fuling District of Chongqing as a case study, the information value model, BP neural network model, random forest model, information value-BP neural network coupled model, and information value-random forest coupled model were used to evaluate regional landslide susceptibility. By comparing the receiver operating characteristic (ROC) curves, area under the curve (AUC), and susceptibility distribution patterns of different models, a critical monthly average rainfall threshold model for landslides is proposed, and critical monthly average rainfall thresholds for different temporal probabilities were inferred. The susceptibility results were coupled with temporal probability levels to produce regional landslide hazard assessment results. The evaluation accuracy is further validated with 30 randomly selected landslide events and 4 typical landslide cases. The results show that coupling the Information Value and machine learning models compensates for the shortcomings of machine learning in early data input and non-sample selection, enhancing the predictive accuracy of single machine learning models. Among these, the information value-random forest coupled model exhibits the highest predictive accuracy; of the 30 randomly selected landslide samples, 20 cases (67%) occurred in areas with a temporal probability of over 50%, validating the accuracy of the critical monthly average rainfall threshold model. The 4 typical landslide samples selected randomly were primarily in the P4 or P5 temporal probability levels and were located in high to very high-risk areas, aligning well with field survey results. This indicates that the regional landslide hazard assessment based on the information value-random forest coupled model and the critical monthly average rainfall threshold is accurate and reliable.
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表 1 基于易发性与时间概率等级的区域滑坡危险性评价表
Table 1. Regional landslide hazard assessment table based on susceptibility and temporal probability levels
时间等级 易发性 极低
易发性低
易发性中
易发性高
易发性极高
易发性P1(0<P(x)≤P1) 极低
危险性极低
危险性极低
危险性极低
危险性低
危险性P2(P1<P(x)≤P2) 极低
危险性极低
危险性低
危险性低
危险性中
危险性P3(P2<P(x)≤P3) 极低
危险性低
危险性中
危险性中
危险性高
危险性P4(P3<P(x)≤P4) 极低
危险性低
危险性中
危险性高
危险性极高
危险性P5(P4<P(x)≤1) 低
危险性中
危险性高
危险性极高
危险性极高
危险性
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表 2 评价因子分级结果
Table 2. The grading results of assessment factors
评价
因子分级 分级面积/km2 Si/S
(×100)滑坡面积/km2 Ni/N
(×100)I 坡度/（°） 0～10 997.6 33.9 1.8 22.8 −0.4 10～20 996.8 33.9 3.7 46.8 0.3 20～30 629.7 21.4 2.0 25.3 0.2 30～40 246.3 8.4 0.4 5.1 −0.5 >40 71.8 2.4 0.0 0.0 −5.3 坡向/
（°）北(337.5～22.5) 168.2 5.7 0.3 3.8 −0.4 东北(22.5～67.5) 67.7 2.3 1.5 19.0 2.1 东(67.5～112.5) 320.3 10.9 1.0 12.7 0.2 东南(112.5～157.5) 337.5 11.5 0.8 10.1 −0.1 南(157.5～202.5) 294.5 10.0 0.8 10.1 0.0 西南(202.5～247.5) 313.1 10.6 0.8 10.1 0.0 西(247.5～292.5) 352.2 12.0 1.3 16.5 0.3 西北(292.5～337.5) 400.8 13.6 1.1 13.9 0.0 平面(−1) 687.7 23.4 0.3 3.8 −1.8 曲率 <−9 100.9 3.4 0.0 0.0 −5.6 −9～−6 172.7 5.9 0.1 1.3 −1.5 −6～−3 1201.1 40.8 3.4 43.0 0.1 −3～0 550.7 18.7 0.3 3.8 −1.6 0～3 550.7 18.7 4.1 51.9 1.0 3～6 176.0 6.0 0.0 0.0 −6.2 6～12 92.9 3.2 0.0 0.0 −5.5 >12 97.7 3.3 0.0 0.0 −5.6 高程/
m<200 129.4 4.4 1.9 24.1 1.7 200～300 377.1 12.8 2.6 32.9 0.9 300～400 449.6 15.3 1.6 20.3 0.3 400～500 417.2 14.2 0.7 8.9 −0.5 500～600 377.3 12.8 0.4 5.1 −0.9 600～700 435.3 14.8 0.4 5.1 −1.1 700～800 375.7 12.8 0.2 2.5 −1.6 800～900 124.1 4.2 0.1 1.3 −1.2 900～ 1000 72.7 2.5 0.0 0.0 −5.3 > 1000 184.1 6.3 0.0 0.0 −6.2 地形湿度指数 <10 558.1 19.0 0.9 11.4 −0.5 10～20 825.4 28.1 2.2 27.8 0.0 20～30 449.1 15.3 1.6 20.3 0.3 30～40 215.4 7.3 1.2 15.2 0.7 40～50 108.0 3.7 0.5 6.3 0.5 >50 786.5 26.7 1.5 19.0 −0.3 地表粗
糙度<1.1 2346.2 79.7 6.7 84.8 0.1 1.1～1.2 400.9 13.6 1.0 12.7 −0.1 1.2～1.3 120.8 4.1 0.2 2.5 −0.5 1.3～1.4 55.9 1.9 0.0 0.0 −5.0 1.4～1.5 15.8 0.5 0.0 0.0 −3.7 >1.5 2.7 0.1 0.0 0.0 −2.0 地层 梁山组+栖霞组+
茅口组并层26.1 0.9 0.0 0.1 −1.9 大冶组与嘉陵江
组并层533.8 18.1 0.3 3.8 −1.6 巴东组 212.6 7.2 1.0 12.7 0.6 地层 吴家坪组与长兴
组并层48.8 1.7 0.0 0.0 −4.9 韩家店组 9.0 0.3 0.0 0.0 −3.2 须家河组 137.7 4.7 0.4 5.1 0.1 珍珠冲组 122.2 4.2 0.7 8.9 0.8 自流井组 85.3 2.9 0.7 8.9 1.1 新田沟组 85.4 2.9 0.2 2.5 −0.1 沙溪庙组 841.7 28.6 2.4 30.4 0.1 龙马溪组与小河坝组并层 2.2 0.1 0.0 0.0 −1.8 蓬莱镇组 367.7 12.5 0.0 0.5 −3.2 遂宁组 469.8 16.0 2.2 27.8 0.6 岩层倾
角/（°）<10 632.0 21.5 1.8 22.8 0.1 10～20 1096.4 37.3 3.9 49.4 0.3 20～30 443.6 15.1 1.1 13.9 −0.1 30～40 364.9 12.4 0.6 7.6 −0.5 40～50 342.2 11.6 0.5 6.3 −0.6 50～60 58.2 2.0 0.0 0.0 −5.1 >60 5.2 0.2 0.0 0.0 −2.6 岩层倾
向/（°）北(337.5～22.5) 376.3 12.8 1.0 12.9 0.0 东北(22.5～67.5) 319.5 10.8 0.7 8.5 −0.2 东(67.5～112.5) 367.2 12.5 1.0 12.5 0.0 东南(112.5～157.5) 364.8 12.4 0.8 9.6 −0.3 南(157.5～202.5) 305.1 10.3 0.5 6.6 −0.4 西南(202.5～247.5) 313.5 10.6 1.5 18.4 0.6 西(247.5～292.5) 427.1 14.5 1.5 18.7 0.3 西北(292.5～337.5) 476.2 16.1 1.0 12.8 −0.2 距断层距离/km <1 101.2 3.4 0.5 6.3 0.6 1～2 111.9 3.8 0.6 7.6 0.7 2～3 128.4 4.4 0.2 2.5 −0.5 3～4 141.3 4.8 0.4 5.1 0.1 4～5 154.4 5.2 0.3 3.8 −0.3 >5 2305.2 78.3 5.9 74.7 0.0 距水系距离/m 0～200 173.6 5.9 2.6 32.4 1.7 200～400 102.1 3.5 1.2 14.9 1.5 400～600 104.7 3.6 0.2 2.5 −0.4 600～800 94.5 3.2 0.2 2.9 −0.1 800～ 1000 102.9 3.5 0.2 2.9 −0.2 1000 ～1200 84.8 2.9 0.1 0.9 −1.2 1200 ～1400 100.2 3.4 0.1 0.8 −1.4 1400 ～1600 90.9 3.1 0.7 8.9 1.1 > 1600 2087.6 71.0 2.7 33.9 −0.7 距道路距离/m 0～200 310.1 10.5 2.1 28.5 1.0 200～400 197.5 6.7 0.5 6.6 0.0 400～600 177.1 6.0 0.8 11.2 0.6 600～800 144.4 4.9 0.4 5.1 0.0 800～ 1000 140.5 4.8 0.2 2.2 −0.8 1000 ～1200 110.4 3.8 0.2 2.9 −0.3 1200 ～1400 122.2 4.2 0.3 4.0 0.0 > 1400 1739.1 59.1 3.4 44.9 −0.3 土层厚
度/m0～2.5 2503.5 85.1 4.0 50.6 −0.5 2.5～5 272.4 9.3 1.3 16.5 0.6 5～7.5 30.5 1.0 0.9 11.4 2.4 7.5～10 69.8 2.4 0.9 11.4 1.6 10～12.5 18.6 0.6 0.2 2.5 1.4 12.5～15 19.6 0.7 0.2 2.5 1.3 15～17.5 13.8 0.5 0.4 5.1 2.4 17.5～20 9.3 0.3 0.0 0.0 −3.2 >20 5.2 0.2 0.0 0.0 −2.6 归一化植被指数 <−0.1 53.1 1.8 0.6 7.6 1.4 −0.1～0 15.8 0.5 0.2 2.5 1.5 0～0.1 24.1 0.8 0.1 1.3 0.4 0.1～0.2 62.9 2.1 0.1 1.3 −0.5 0.2～0.3 135.6 4.6 0.5 6.3 0.3 0.3～0.4 720.1 24.5 1.7 21.5 −0.1 0.4～0.5 1624.4 55.2 3.7 46.8 −0.2 >0.5 306.1 10.4 1.0 12.7 0.2 土地利用类别 耕地 1452.9 49.4 5.3 67.5 0.3 林地 726.6 24.7 0.4 4.7 −1.7 草地 512.1 17.4 1.3 16.6 −0.1 灌木地 67.1 2.3 0.1 1.2 −0.7 湿地 0.3 0.0 0.0 0.1 2.1 水体 88.7 3.0 0.0 0.1 −3.1 人造地表 94.4 3.2 0.8 9.8 1.1
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表 3 雨量站月平均降雨量
Table 3. Average monthly rainfall of rainfall stations
雨量站 月平均降雨量/mm 长寿 95.6 涪陵 91.4 丰都 91.6 凤来 106.4 雨台山 81.9 睦和 87.5 双河口 104.2 大木 117.6 河图 75.6 武陵山 89.3 龙潭 102.5
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表 4 时间概率等级与月平均降雨量关系表
Table 4. Relationship between temporal probability levels and monthly average rainfall
时间概率等级 时间概率 月平均降雨量/mm P1 0<P(x)≤0.1 <78 P2 0.1<P(x)≤0.25 78～87 P3 0.25<P(x)≤0.5 87～96 P4 0.5<P(x)≤0.75 96～106 P5 0.75<P(x)≤1 >106
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表 5 30次滑坡样本发生时间概率等级统计表
Table 5. Temporal probability levels of occurrence for 30 landslide samples
时间概率等级 P1 P2 P3 P4 P5 样本数 2 2 6 12 8
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表 6 4次典型滑坡信息
Table 6. Information on four typical landslides
滑坡事件 日期 月平均降雨量/mm 时间概率等级 滑坡1 2020-06-29 98.7 P4 滑坡2 2018-08-01 103.4 P4 滑坡3 2020-06-27 118.2 P5 滑坡4 2016-06-02 121.3 P5
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