Machine Learning Solution for Landslide Susceptibility Based on Hydrographic Division: Case Study of Fengjie County in Chongqing
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摘要: 三峡库区是地质灾害管理的重点地区，鉴于长江对其沿岸边坡的水力作用不容忽视，因此需进一步研究水系因素对滑坡易发性的影响.以重庆市奉节县为例，考虑区域内水系影响显著，沿水域两岸300 m区域内划分为分区Ⅰ，其余区域为分区Ⅱ.其次，全域、分区Ⅰ、分区Ⅱ以提取的16个影响因子建立易发性评价指标分析模型，基于随机森林模型计算区域滑坡发生概率，并将全域和分区的滑坡易发性评价结果对比分析.结果表明：奉节县高和极高易发区主要分布在水域两岸及耕地范围内，这是由于库水位升降减少了防滑截面的有效应力，由于原有山体平衡在垦荒过程中被破坏，耕地对斜坡的防护作用微弱；基于水系分区后模型的训练精度优于全域模型的训练精度，准确率和F1分数的最大提升幅度分别可达5.1%、5.2%.基于水系分区的方法有利于提高滑坡易发性评价精度，该方法实用性强，可靠性高.Abstract: The Three Gorges Reservoir Area is the key area for geological disaster management, and the hydraulic effect of the Yangtze River on the slopes along its banks cannot be ignored. Therefore, it is necessary to study the influence of drainage factors on landslide susceptibility. The historical landslides points in Fengjie County and their corresponding features are taken as analysis data. Due to the significant influence of regional water system, the research area is divided into two sub-zones according to hydrographic conditions. Area of 300 meters along the two sides of the rivers is regarded as Sub-Zone Ⅰ, and the remaining area is defined as Sub-Zone Ⅱ. Then, a total 16 influencing factors are selected to establish landslide susceptibility evaluating models, and the landslide susceptibility evaluation results of the whole region and sub-zones were compared and analyzed. The following results of landslide susceptibility analysis based on machine learningalgorithm can be obtained. Because the fluctuation of reservoir water level reduces the effective stress of anti-slip section and the cultivated land has weak conservation effect on slope mass owing to the destruction of the original mountain balance in the process of reclamation, the areas with high and extremely high probability of landslide occurrences in Fengjie County mainly lie on the bank of rivers and in the area of cultivated land. The accuracy of susceptibility assessment of the hydrographic-divided model is better than the whole-range model. Specifically, the accuracy and F-score are improved by 5.1% and 5.2%, which indicates the practicability and validity of conducting zone-dividing susceptibility analysis.
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图 3 基于水系分区的滑坡易发性评价流程
Fig. 3. Flowchart of the landslide susceptibility based on hydrographic division
图 4 奉节县分区示意及历史滑坡分类
a.分区示意图；1.长江；2.草堂河；3.梅溪河；4.朱衣河；5.安坪河；6.大溪河；7.崔家河；8.花园河；9.石马河；10.车家坝河；11.甲高河；12.石荀河；13.新民河；14.大洞河；15.羊圈河；16.撒谷溪；17.高治河；18.竹坪溪；19.杨坪沟；20.双岔河；21.清泉河；b.滑坡规模分类；c. 滑坡类型分类
Fig. 4. Zonation map of Fengjie County and classification of historical landslides
图 8 模型成功率曲线及混淆矩阵
a.ROC曲线；b.全域；c.分区Ⅰ；d.分区Ⅱ
Fig. 8. Validation AUC values and confusion matrixes of different models
表 1 数据来源
Table 1. Data and data sources
数据名称 来源 类型 精度 历史滑坡 重庆市地质环境监测站 数据表 高程 地理空间数据云 栅格 30 m 岩性 全国地质资料馆 矢量 1∶20万 卫星图像 MODIS中国合成产品 栅格 30 m 土地利用类型 GlobeLand30 栅格 30 m 水系 水利局 矢量 1∶10万 道路 交通委 矢量 1∶10万 POI python爬虫 数据表 年平均降雨量 中国气象网 数据表 
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表 2 影响因子重分类标准
Table 2. Reclassification criteria of factors affecting landslides
因子 分级 因子 分级 因子 分级 高程(m) 1. < 250 坡向 1. 平面 8. 西 TWI 1. < 5 2. 250~500 2. 北 9. 西北 2. 5~10 3. 500~750 3. 东北 3. 10~15 4. 750~1 000 4. 东 4. 15~20 5. 1 000~1 500 5. 东南 5. ≥20 6. 1 500~2 000 6. 南 7. ≥2 000 7. 西南 坡度(°) 1. < 6 平面曲率 1. < -0.01 RDLS 1. 0~20 2. 6~12 2. -0.01~0.01 2. 20~40 3. 12~18 3. ≥0.01 3. 40~60 4. 18~24 剖面曲率 1. < -0.01 4. 60~80 5. 24~34 2. -0.01~0.01 5. 80~100 6. 34~44 3. ≥0.01 6. 100~120 7. ≥44 7. ≥120 距断层距离(m) 1. < 500 岩性 1. S1-2 9. T1j 距构造距离(m) 1. < 500 2. 500~1 000 2. P2 10. J1z 2. 500~1 000 3. 1 000~1 500 3. T1-2j 11. J2xs 3. 1 000~1 500 4. 1 500~2 000 4. P1 12. T3xj 4. 1 500~2 000 5. 2 000~2 500 5. T1d 13. J2s 5. 2 000~2 500 6. 2 500~3 000 6. T2b 14. J3s 6. 2 500~3 000 7. ≥3 000 7. J2x 15. J3p 7. ≥3 000 8. J1-2z 16.不明 距水系距离(m) 1. < 200 NDVI 1. < -0.25 年平均降雨量(mm) 2. 200~400 2. ≥-0.25~0 1. < 1 190 3. 400~600 3. ≥0~0.25 2. 1 190~1 235 4. 600~800 4. ≥0.25~0.5 3. 1 235~1 280 5. 800~1 000 5. ≥0.5~0.75 4. 1 280~1 325 6. 1 000~1 200 6. ≥0.75~1.00 5. 1 325~1 370 7. 1 200~1 400 土地利用类型 1. 人造地表 6. 1 370~1 415 8. 1 400~1 600 2. 林地 5. 草地 7. 1 415~1 450 9. 1 600~1 800 3. 水体 10. ≥1 800 4. 耕地 距道路距离(m) 1. < 100 7. 600~700 POI核密度 1. < 2 000 2. 100~200 8. 700~800 2. 2 000~20 000 3. 200~300 9. 800~900 3. 20 000~50 000 4. 300~400 10. 900~1 000 4. 50 000~100 000 5. 400~500 11. ≥1 000 5. 100 000~200 000 6. 500~600 6. ≥200 000
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表 3 易发性分区中滑坡频率信息统计
Table 3. Statistic results of landslide susceptibility in different levels of different models
模型 滑坡易发性分区 滑坡敏感性阈值 滑坡比率(%) 面积比率(%) 频率比(%) 全域 极低易发区 0~0.145 0.56 32.29 0.017 低易发区 0.145~0.345 1.18 25.44 0.047 中易发区 0.345~0.557 4.17 17.97 0.232 高易发区 0.557~0.769 16.82 14.34 1.173 极高易发区 0.769~1.000 77.26 9.96 7.758 分区Ⅰ 极低易发区 0~0.161 0.00 25.26 0.000 低易发区 0.161~0.333 1.52 22. 76 0.067 中易发区 0.333~0.510 2.03 21.37 0.095 高易发区 0.510~0.706 8.88 19.05 0.466 极高易发区 0.706~1.000 87.56 11.56 7.576 分区Ⅱ 极低易发区 0~0.149 0.82 29.99 0.027 低易发区 0.149~0.337 2.39 25.79 0.093 中易发区 0.337~0.545 4.12 19.49 0.212 高易发区 0.545~0.757 14.92 14.21 1.050 极高易发区 0.757~1.000 77.74 10.51 7.394
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表 4 三种模型的表现结果
Table 4. The performance results of the three models
模型 评价指标 AUC 准确率 精确率 召回率 F1分数 全域 0.777 0.767 0.788 0.777 0.850 分区Ⅰ 0.817 0.822 0.813 0.818 0.897 分区Ⅱ 0.813 0.811 0.803 0.807 0.885
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