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浙江大学学报(工学版)  2021, Vol. 55 Issue (3): 472-482    DOI: 10.3785/j.issn.1008-973X.2021.03.007
土木与交通工程     
基于决策树和有效降雨强度的滑坡危险性预警
黄发明(),曹中山,姚池(),姜清辉,陈佳武
1. 南昌大学 建筑工程学院,江西 南昌 330031
2. 武汉大学 土木建筑工程学院,湖北 武汉 430000
Landslides hazard warning based on decision tree and effective rainfall intensity
Fa-ming HUANG(),Zhong-shan CAO,Chi YAO(),Qing-hui JIANG,Jia-wu CHEN
1. School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031
2. School of Civil Engineering, Wuhan University, Wuhan, 430000
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摘要:

以江西省寻乌县为例,采用信息量(IV)、反向传播神经网络(BPNN)和C5.0决策树模型进行滑坡易发性预测(LSP),比较不同模型的预测性能;基于有效降雨量的概念提出有效降雨强度-历时(EI-D)模型,计算滑坡临界降雨阈值并将其与传统的降雨强度-历时(I-D)阈值做对比;将LSP结果与EI-D模型耦合,实现滑坡灾害预警并进一步验证了预警精度. 结果表明:C5.0决策树的LSP精度高于BPNN和IV,EI-D阈值的预测效果优于I-D模型,且基于滑坡易发性和EI-D阈值的模型能有效实现降雨型滑坡的实时预报.

关键词: 降雨型滑坡滑坡危险性预警滑坡易发性预测(LSP)有效降雨强度C5.0决策树    
Abstract:

Information value (IV), back-propagation neural network (BPNN) and C5.0 decision tree models were used to implement landslide susceptibility prediction (LSP) for comparisons by taking the Xunwu County of Jiangxi Province as a case, EI-D model was proposed to calculate the critical rainfall thresholds of these landslides based on the concept of early effective rainfall. The results of EI-D were compared with the results of conventional I-D model for uncertainty analysis. LSP results were coupled with the EI-D model to realize the landslides hazard warning with the warning accuracy further verified. Results show that the C5.0 decision tree has higher LSP accuracy than BPNN, followed by IV model. The temporal probability prediction accuracy of EI-D model is superior to the I-D model. The present model based on LSP and EI-D can effectively achieve real-time rainfall-induced landslides early warning.

Key words: rainfall-induced landslide    landslide hazard warning    landslide susceptibility prediction (LSP)    effective rainfall intensity    C5.0 decision tree
收稿日期: 2020-01-18 出版日期: 2021-04-25
CLC:  P 694  
基金资助: 国家自然科学基金资助项目(41807285,41762020,51879127,51769014);中国博士后基金资助项目(2019M652287);江西省自然科学基金资助项目(20192BAB216034,20192ACB2102,20192ACB20020);江西省博士后基金资助项目(2019KY08)
通讯作者: 姚池     E-mail: faminghuang@ncu.edu.cn;chi.yao@ncu.edu.cn
作者简介: 黄发明(1988—),男,副教授,从事滑坡危险性预警的研究. orcid.org/0000-0002-4428-7133. E-mail: faminghuang@ncu.edu.cn
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引用本文:

黄发明,曹中山,姚池,姜清辉,陈佳武. 基于决策树和有效降雨强度的滑坡危险性预警[J]. 浙江大学学报(工学版), 2021, 55(3): 472-482.

Fa-ming HUANG,Zhong-shan CAO,Chi YAO,Qing-hui JIANG,Jia-wu CHEN. Landslides hazard warning based on decision tree and effective rainfall intensity. Journal of ZheJiang University (Engineering Science), 2021, 55(3): 472-482.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.03.007        http://www.zjujournals.com/eng/CN/Y2021/V55/I3/472

T1(叠加S1~S5 T2(叠加S1~S5 T3(叠加S1~S5 T4(叠加S1~S5 T5(叠加S1~S5
不预警区(1级) 不预警区(1级) 不预警区(1级) 不预警区(1级) 不预警区(2级)
不预警区(1级) 不预警区(1级) 不预警区(2级) 2级预警区 3级预警区
不预警区(1级) 不预警区(2级) 3级预警区 3级预警区 4级预警区
不预警区(1级) 不预警区(2级) 3级预警区 4级预警区 5级预警区
不预警区(2级) 3级预警区 4级预警区 5级预警区 5级预警区
表 1  基于易发性和降雨诱发滑坡概率的滑坡危险性预警级别
预警级别 当日滑坡发生情况 相应措施
Ⅰ级或不预警 可能性极小,无危害 无需采取措施
Ⅱ级或不预警 可能性较小,基本无危害 对重要滑坡隐患点定时监测
Ⅲ级预警(注意) 可能性中等,规模和危害中等 注意监测滑坡,采取防御措施,提醒灾区人员关注灾害动态
Ⅳ级预警(预警) 可能性较大,规模或危害较大 加强对灾害点的监测,对滑坡危险区应开展预防应急措施
Ⅴ级预警(警报) 可能性很大,规模或危害严重 全天候监测滑坡,建立防御措施、救灾体系和紧急疏散通道等
表 2  预警分级的意义及应对措施
图 1  寻乌县位置及滑坡编录
图 2  滑坡相关环境因子图(无坡向和剖面曲率)
环境因子 属性区间 因子类型 全区格栅 滑坡格栅 频率比
数量 占比/% 数量 占比/%
坡度/(°) 0~4.8 连续型 399 507 15.57 125 3.86 0.25
坡度/(°) 4.8~8.6 连续型 526 595 20.52 657 20.31 0.99
坡度/(°) 8.6~12.4 连续型 528 932 20.62 932 28.81 1.40
坡度/(°) 12.4~16.1 连续型 434 885 16.95 721 22.29 1.31
坡度/(°) 16.1~20.1 连续型 322 276 12.56 475 14.68 1.17
坡度/(°) 20.1~24.5 连续型 203 715 7.94 228 7.05 0.89
坡度/(°) 24.5~30.2 连续型 111 214 4.34 81 2.50 0.58
坡度/(°) 30.2~53.4 连续型 38 661 1.51 16 0.49 0.33
距河流距离/m 900~3 000 离散型 884 820 34.49 483 14.93 0.43
距河流距离/m 600~900 离散型 477 428 18.61 362 11.19 0.60
距河流距离/m 300~600 离散型 563 869 21.98 538 16.63 0.76
距河流距离/m 0~300 离散型 639 627 24.93 1 852 57.25 2.30
岩土类型 变质岩类 离散型 592 129 23.08 1 050 32.46 1.41
岩土类型 碳酸岩类 离散型 1 727 958 67.35 2 036 62.94 0.93
岩土类型 碎屑岩类 离散型 245 695 9.58 149 4.61 0.48
NDVI 0~0.33 连续型 10 451 0.41 6 0.19 0.46
NDVI 0.33~0.45 连续型 48 261 1.88 33 1.02 0.54
NDVI 0.45~0.55 连续型 112 791 4.40 201 6.21 1.41
NDVI 0.55~0.63 连续型 287 167 11.19 582 17.99 1.61
NDVI 0.63~0.67 连续型 526 616 20.53 805 24.88 1.21
NDVI 0.67~0.73 连续型 661 306 25.77 876 27.08 1.05
NDVI 0.73~0.80 连续型 568 890 22.17 554 17.13 0.77
NDVI 0.80~1.00 连续型 350 303 13.65 178 5.50 0.40
表 3  环境因子频率比
图 3  3个模型的AUC精度
图 4  各模型预测的滑坡易发性图
图 5  1970~2010年月均降雨量和相应滑坡发生频数
$\alpha $ R0 $R_1^\prime $ $R_2^\prime $ $R_3^\prime $ $R_4^\prime $ $R_5^\prime $ $R_6^\prime $ $R_7^\prime $ $R_8^\prime $ $R_9^\prime $
注:**表示在0.01显著性水平下,显著相关.
0.9 0.517** 0.561** 0.604** 0.612** 0.613** 0.607** 0.585** 0.570** 0.560** 0.558**
0.8 0.517** 0.567** 0.612** 0.621** 0.623** 0.622** 0.611** 0.604** 0.602** 0.601**
0.7 0.517** 0.564** 0.612** 0.627** 0.623** 0.620** 0.614** 0.607** 0.605** 0.605**
0.6 0.517** 0.561** 0.607** 0.622** 0.613** 0.608** 0.609** 0.609** 0.609** 0.609**
0.5 0.517** 0.557** 0.598** 0.611** 0.601** 0.602** 0.601** 0.601** 0.601** 0.601**
表 4  不同降雨强度系数和累计降雨量与滑坡相关性
图 6  2种阈值模型分级图
图 7  2种阈值模型精确度
降雨时间 Rc /mm Re /mm
第一日 57.27 57.27
第二日 92.38 107.42
第三日 118.75 161.23
第四日 140.27 223.97
表 5  滑坡危险性预警宏观阈值
滑坡发生日期 R7 /mm R6 /mm R5 /mm R4 /mm R3 /mm R2 /mm R1 /mm R0 /mm Rc /mm EI /(mm·d?1 D /d
1991?06?21 4.0 0.0 0.0 0.0 23.6 69.9 42.8 45.8 118.1 29.5 4
2000?06?20 0.0 0.0 0.0 0.0 19.4 67.5 61.0 18.5 100.9 25.2 4
2000?07?07 1.9 0.0 0.2 0.0 0.0 28.6 46.1 46.9 93.2 31.1 3
2000?08?25 0.3 0.0 0.0 0.0 0.0 10.5 23.2 143.0 164.4 54.8 3
2001?06?05 0.0 18.5 0.0 0.0 0.0 36.0 74.0 41.6 111.0 37.0 3
2002?07?02 0.0 0.0 0.0 0.0 0.0 21.9 41.0 42.0 81.4 27.1 3
2003?05?17 23.5 0.0 0.0 4.9 14.0 9.1 4.2 106.1 119.5 23.9 5
2004?04?24 0.5 0.0 0.0 0.0 28.3 40.6 79.8 5.6 91.1 22.8 4
2004?06?12 0.3 0.0 10.4 43.2 49.5 23.4 31.2 31.1 93.5 15.6 6
2004?07?08 10.1 1.0 0.0 0.0 0.0 0.0 0.0 77.4 77.4 77.4 1
表 6  各滑坡发生日期前期降雨量
图 8  样本滑坡的时间概率分布
图 9  各降雨阈值依次为T1到T5时的寻乌县滑坡危险性预警图
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[1] 黄发明, 殷坤龙, 张桂荣, 唐志政, 张俊. 多变量PSO-SVM模型预测滑坡地下水位[J]. 浙江大学学报(工学版), 2015, 49(6): 1193-1200.