Application of different machine learning models in landslide susceptibility assessment in Badong County, Hubei province
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摘要:
中国是世界上发生滑坡灾害最频繁的国家之一,滑坡易发性评价有助于防灾减灾工作。由于不同机器学习模型在不同区域的适配程度不同,为更好开展湖北省巴东县的滑坡灾害防治工作,选取坡度、坡向、曲率、起伏度、地层、覆盖层、NDVI、道路密度、水系密度、斜坡结构10个影响因子,采用逻辑回归(LR)、支持向量机(SVM)、多层感知机(MLP)和随机森林(RF)四种模型进行滑坡易发性评价。并通过受试者工作特征曲线(ROC)、均方误差与决定系数等指标、滑坡-研究区占比三种评价方式用于评价模型精度。实验结果表明,不同模型在不同评价方式中存在差异,但总体而言,RF模型精度最高且绘制出的易发性分区图更合理。四个模型绘制的易发性区域分布图相似,极高易发区和高易发区主要分布于南边沿江地区,西南沿岸的官渡口镇、焦家湾村等附近地区表现出较高易发性,该评价结果可以为巴东县的滑坡治理提供参考。
Abstract:China is one of the countries most frequently affected by landslide disasters in the world, making landslide susceptibility assessment crucial for effective disaster prevention and mitigation. Due to variations in the adaptability of different machine learning models in different regions, in order to better carry out landslide disaster prevention and control work in Badong County, Hubei Province, ten influencing factors including slope gradient, slope direction, curvature, degree of undulation, stratigraphy, overburden, NDVI, road density, water system density, and slope structure were selected. Four different models, including Logistic Regression (LR), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Random Forest (RF), were used for landslide susceptibility evaluation. Three evaluation methods were used to assess the accuracy of the model: Receiver Operating Characteristic (ROC) curves, mean square error, determination coefficient, and the ratio of landslide to study area. The experimental results show that there are differences among the models in different evaluation methods. Overall, the RF model exhibits the highest accuracy and generates more reasonable susceptibility zoning maps. The susceptibility distribution maps generated by the four models are similar, with high and very high susceptibility areas predominantly located in the southern riverside area. Areas near Guandukou Town and Jiaojiawan Village along the southwest coast exhibit relatively high susceptibility. The assessment results can provide reference for landslide control in Badong County.
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0. 引言
从统计数据分析得出,100%的泥石流、90%的滑坡和81%的崩塌由降水诱发[1],地质灾害的发生与降水关系密切[2 − 3],因此地质灾害气象风险预警是防灾减灾的有效手段之一。自2003年起,自然资源部(原国土资源部)与中央气象台启动并开展了国家级地质灾害气象风险预警业务[4 − 6],同年,四川省率先启动省级地质灾害气象风险预警工作。截至2018年,四川省省级预警系统是单机版内网运行,预警产品需从内网拷贝通过外网发布,21个市(州)175个地质灾害易发县(市、区)中,仅成都、雅安、攀枝花、凉山4个市(州),青川1个县建立了地质灾害气象风险预警平台,可开展本级地质灾害气象风险预警预报工作,大部分市(州)、县(市、区)主要转发上级预警结果或依靠本级气象部门发布预警信息,不具备独立的地质灾害气象风险预警分析、制作、发布的能力,省市县三级预警业务独立运行、各自为阵;由于市县大多缺乏必要的预警平台和预警模型,导致预警精准度较低,预警响应呈现全省“上下一般粗”的情况;防灾一线同一时间重复接收省市县各级发布的预警信息,造成信息冗杂混乱,难以高效应对。2019年开始,四川省加强统筹谋划,充分运用多年开展地质灾害气象风险预警工作成果和实践经验,联合中国地质环境监测院(自然资源部地质灾害技术指导中心),按照“省级建设管理,省市县统一平台互联运行”的工作思路,逐步建立分级预警工作机制,同步建成四川省地质灾害气象风险预警互联系统。于2022年汛期开启四川省地质灾害气象风险预警“省市县一体化”运行模式。通过2年的运行情况来看,实现省市县三级各节点用户登录账户独立运行,为地质灾害预警信息存储、管理、分析、传输、发布等提供一个高效、稳定、安全的互联互通环境,实现了资源的集约化,有力支撑省市县地质灾害防灾减灾管理决策服务,提高预警预报工作效率,增强抢险救灾和应急处突的预警保障能力。
本文从四川省地质灾害气象风险预警业务运行的实践出发,面对各级预警业务独立运行的状态、基层缺平台缺经费缺技术、多头发布预警信息给基层防灾工作造成困扰等问题,系统介绍了四川省地质灾害气象风险预警“省市县一体化”建设思路,预警模型建立的过程,预警互联系统架构与功能,以及全省运行的效果。
1. 四川省地质灾害气象风险预警“省市县一体化”建设思路
为有效提升地质灾害气象风险预警能力,四川省探索建立地质灾害气象风险预警“省市县一体化”运行模式,主要包括三个方面:一是分级预警机制的建立,在预警产品精度上,省级预警到市,精度到县;市级预警到县,精度到乡;县级预警到乡,精度到村;在预警信息发布上,实行省级预警发布到市县两级政府及自然资源主管部门、省级有关行业主管部门(单位);市级发布到县级自然资源主管部门和市级有关行业主管部门;县级发布到乡村两级防灾责任人和县级有关行业主管部门及隐患点防灾监测责任人、在建工程防灾责任人。二是四川省地质灾害气象风险预警模型研究,利用统计学理论,分别采用隐式统计预报法和显示统计预报法[7],研发了适合四川省的临界降雨阈值模型和基于地质灾害危险性的预警模型,构建了预警精度为1 km网格的地质灾害气象风险预警模型。三是四川省地质灾害气象风险预警互联系统建设,系统采用B/S架构,统一了数据库和地图建设标准,通过信息共享管理和多租户技术实现多级用户使用和维护一个平台,系统运行于互联网环境下,在省级层面实现气象数据的实时处理,核心功能包括预警分析、成果发布、模型管理、运维管理以及三级的互联互动和数据共享交换等功能模块,构建了全省统一的分布式模型集群和模拟计算引擎,实现“省级建设、多级应用”。
2. 四川省地质灾害气象风险预警模型研发
四川省位于中国大陆地势三大阶梯中的第一级和第二级,地势西高东低,区域地形复杂多样,河谷深切,高差巨大,地层岩性及地质构造复杂,地震活动频繁。受暖湿的亚热带东南季风和干湿季分明的亚热带西南季风交替影响,东西部气候特征差异显著,东部的四川盆地为夏季高温多雨,冬季温和少雨,年均降水量约1000 mm;西部的川西高原气候垂直分布现象明显,年均降水量500~700 mm。据不完全统计,2013—2022年四川共发生地质灾害28335处,造成373人死亡(失踪),86人受伤,直接经济损失85.25亿元。
2.1 临界降雨阈值模型
根据四川省地貌格局、地质灾害易发分区以及历史地质灾害预警区划,将四川省分为10个预警大区、18个预警亚区(图1),分亚区开展临界降雨阈值模型数据分析研究。
本次临界降雨阈值模型采用激发雨量(x)—前期有效雨量(y)临界降水判据开展研究[7 − 8],假定阈值曲线为线性函数,阈值模型曲线示意图如图2所示,阈值模型通式为:
$$ y=Kx+B $$ (1) 式中:y——前期有效雨量;
K——斜率参数;
x——当日激发雨量;
B——截距参数。
基于临界降雨阈值模型的地质灾害气象风险预警对象为降水引发的区域群发型崩塌、滑坡、泥石流等地质灾害,通过筛选2013—2020年具有确定经纬度坐标和发生时间的地质灾害数据5273条,作为预警模型构建的基本统计样本,同时利用“时空约束条件下随机采样”[9 − 12]方法,建立样本库。在四川省地质灾害预警区划基础上,利用当日激发雨量(x)与前期有效降水雨量(y),降雨数据使用气象部门提供的24 h的QPE网格实况数据,数据空间精度为0.05°×0.05°,分区(18个亚区)建立临界降水阈值模型见表1,部分样本不足区域参考相邻预警区模型使用。
表 1 各预警区临界降雨阈值判据Table 1. Critical rainfall threshold criterion for each early warning area预警
分区预警等级 K B 预警
分区预警等级 K B Ⅰ 红色预警 −0.44 82.8 Ⅶ1 红色预警 −0.45 58.0 橙色预警 −0.44 60.9 橙色预警 −0.45 51.5 黄色预警 −0.44 43.7 黄色预警 −0.45 43.0 Ⅱ1 红色预警 −1.24 138.9 Ⅶ2 红色预警 −0.34 80.4 橙色预警 −1.24 92.4 橙色预警 −0.34 61.7 黄色预警 −1.24 66.1 黄色预警 −0.34 43.3 Ⅱ2 红色预警 −0.79 76.1 Ⅷ1 红色预警 −0.53 36.6 橙色预警 −0.79 67.7 橙色预警 −0.53 35.5 黄色预警 −0.79 56.1 黄色预警 −0.53 34.7 Ⅲ1 红色预警 −0.23 160.4 Ⅷ2 红色预警 −0.38 60.5 橙色预警 −0.23 70.1 橙色预警 −0.38 50.2 黄色预警 −0.23 59.8 黄色预警 −0.38 43.2 Ⅲ2 红色预警 −0.33 136.3 Ⅷ3 红色预警 −0.49 47.2 橙色预警 −0.33 95.8 橙色预警 −0.49 46.0 黄色预警 −0.33 58.4 黄色预警 −0.49 35.0 Ⅳ 红色预警 −0.50 51.6 Ⅸ1 红色预警 −0.15 36.2 橙色预警 −0.50 44.0 橙色预警 −0.15 32.9 黄色预警 −0.50 40.7 黄色预警 −0.15 27.1 Ⅴ1 红色预警 −0.41 122.5 Ⅸ2 红色预警 −0.31 71.4 橙色预警 −0.41 99.2 橙色预警 −0.31 58.5 黄色预警 −0.41 76.1 黄色预警 −0.31 42.1 Ⅴ2 红色预警 −1.42 171.3 Ⅹ1 红色预警 −1.31 45.9 橙色预警 −1.42 132.6 橙色预警 −1.31 40.9 黄色预警 −1.42 113.4 黄色预警 −1.31 40.4 Ⅵ 红色预警 −0.32 105.9 Ⅹ2 红色预警 −0.88 50.8 橙色预警 −0.32 70.4 橙色预警 −0.88 49.6 黄色预警 −0.32 51.4 黄色预警 −0.88 43.7 2.2 基于地质灾害危险性的预警模型研发
在基于地质灾害危险性的预警模型研究中[13 − 14],构建地质灾害潜势度模型的地质环境因子综合选取高程、起伏度、坡度、地层岩性、断层断裂、土地类型、年均雨量、水系、道路、房屋建筑等10类影响因子,定量化取其确定系数值和权重后,经计算得到研究区地质灾害潜势度Q,采用归一化法将其归一化到0~1,再根据四川省年均降雨特征和灾情数据匹配的降水数据,采用专家评判法对四川省地质灾害潜势度进行分区,将四川省地质灾害潜势度分为龙门山断裂带、川东、川南、川西和川东北5个区(图3),通过潜势度分区结果分析,川东北和川东潜势度最高,龙门山断裂带、川南次之,川西潜势度相对较低。从地质灾害“潜势度”灾害点密度分布情况分析(图4),随着地质灾害潜势度值的逐步增大,地质灾害的发生密度整体呈逐步增大趋势,反映了在潜势度值小的区域地质灾害点分布频率小,潜势度值大的区域地质灾害点分布频率大,结果表明地质环境条件较差的区域地质灾害发生数量较多,潜势度计算结果比较合理。利用四川省气象部门提供的24 h的QPE(quantitative precipitation estimation)网格实况数据,对各分区进行前期有效降水量归一化分析,根据潜势度各分区降水量特征,结合专家评判法,得到各潜势度分区内有效降雨计算天数和降雨诱发因子量化赋值见表2。龙门山断裂带、川西有效降雨天计算为累计3 d,其余地区为2 d。川东降雨诱发地质灾害的有效雨量(x+y)量化值最高,龙门山断裂带、川东北、川南依次减小,川西最低。
表 2 各潜势度分区内降水诱发因子量化赋值Table 2. Quantitative assignment of precipitation-inducing factors in each potential degree zone龙门山
断裂带有效雨量计算时间/d 3 有效雨量/mm <100 100~120 120~200 200~250 250~300 ≥300 归一化值(T) 0.2 0.4 0.7 0.8 0.9 1 川东 有效雨量计算天数/d 2 有效雨量/mm <120 120~180 180~400 ≥400 归一化值(T) 0.2 0.4 0.7 1
川南有效雨量计算天数/d 2 有效雨量/mm <50 50~80 80~120 120~150 150~200 ≥200 归一化值(T) 0.2 0.4 0.6 0.8 0.9 1 川西 有效雨量计算天数/d 3 有效雨量/mm <30 30~50 50~80 80~100 ≥100 归一化值(T) 0.2 0.3 0.5 0.8 1 川东北 有效雨量计算天数/d 2 有效雨量/mm <80 80~100 100~150 150~250 ≥250 归一化值(T) 0.2 0.3 0.5 0.8 1 将地质灾害潜势度(Q)评价结果和有效雨量归一化值(T)结果,带入显示预警模型式(2),得到各预警区预警指数(R)。根据灾害发生概率,划分为红色预警、橙色预警、黄色预警和不预警区,划分依据见表3。龙门山断裂带、川东、川南、川东北地区不同预警等级的预警指数R间断点依次为0.3,0.6,0.9;川西地区预警指数R相对较小,为0.1,0.3,0.5。
表 3 各潜势度分区内显式统计预警模型判据Table 3. Explicit statistical early warning model criterion in each potential degree zone预警等级 潜势度分区
预警指数(R)龙门山 断裂带 川东 川南 川西 川东北 不预警 ≤0.3 ≤0.3 ≤0.3 ≤0.1 ≤0.3 黄色预警 0.3~0.6 0.3~0.6 0.3~0.6 0.1~0.3 0.3~0.6 橙色预警 0.6~0.9 0.6~0.9 0.6~0.9 0.3~0.5 0.6~0.9 红色预警 >0.9 >0.9 >0.9 >0.5 >0.9 $$ {{R}}=Q\cdot T $$ (2) 式中:R——潜势度分区的地质灾害气象预警指数;
Q——潜势度分区的地质灾害潜式度评价指标;
T——区域地质灾害的引发因子指数,降雨诱发的 区域地质灾害预警,引发因子特指降水,有 效雨量归一化值。
3. 四川省地质灾害气象风险预警互联系统建设
3.1 系统架构
四川省地质灾害气象风险预警互联系统自底向上可分为:基础设施层、数据管理层、服务支撑层、业务应用层和用户层(图5)。
①基础支撑信息平台:主要是基于自然资源业务网和四川省建成的信息平台的硬件设备、网络设备和基础软件平台,为地质灾害区域气象预警工作提供基础支撑。
②数据管理层:包括空间业务数据、地质灾害数据、雨量数据、气象预警业务数据等,在数据资源层实现数据的采集、整理入库、数据接入、数据存储、数据管理和数据共享。
③共享服务支撑层:对整个应用层的各个应用系统提供支撑服务,涵盖用户权限管理、基础GIS服务、业务应用服务、系统监控服务等。
④业务应用层:围绕省级-地市级-县级“多级递进、逐级精细化”的应用需求,针对不同的系统用户划分为:地质灾害气象风险预警业务模块、地质灾害预警模型管理模块、地质灾害气象预警成果发布模块、地质灾害气象预警体系管理及运维模块。
⑤用户层:用户包括地质灾害管理机构、社会公众和管理及维护人员等群体。其中地质灾害管理机构用户包括三个级别:省级用户、地市级用户、县级用户。
3.2 系统功能
该系统功能包括4大块:地质灾害气象预警业务模块、地质灾害预警模型管理模块、地质灾害气象预警成果发布模块、地质灾害气象预警体系管理及运维模块。省市县三级气象风险预警业务流程见图6。
①地质灾害气象预警业务模块:预警分析的功能是本系统的核心功能,为用户提供制作气象风险预警分析产品的工具,实现预警图的创建、编辑以及相关简报的制作和成果上报等功能。预警分析的操作步骤为预警创建-预警编辑-简报制作-成果提交,各级用户可独立登录统一平台独立分析计算生成预警成果,用户间互不影响,上下级预警成果可互相查看。
②地质灾害预警模型管理模块:本模块主要管理模型的独立系统信息,使其可以被预警分析模块调用。需管理的内容包括模型名称、启动路径或调用接口名称、参数信息表或文件名等信息、修改参数的界面URL地址等。目前该模块中有3类模型:临界雨量阈值模型、基于地质灾害危险性的显示模型、指数T模型(2016年建立的省级模型),用户在预警分析过程中可任选其中一类模型进行计算。
③地质灾害气象预警成果发布模块:预警发布分为自动通知预警区、手动发送短信、发送邮箱、上传至服务器。该功能实现了省级预警到市、市级预警到县、县级预警到乡、村、隐患点。
④地质灾害气象预警体系管理及运维模块:包括隐患点管理、短信模板管理、用户管理、角色管理、菜单管理、部门管理、岗位管理、字典管理、参数设置、日志管理,以及整个系统各功能的运维。
4. 四川省地质灾害气象风险预警“一体化”运行效果
4.1 平台和机制的迭代更新
经过20年的实践,四川省地质灾害气象风险预警工作在平台和机制方面经历了从无到有、从粗到细、从单一到丰富的过程。
在预警平台方面,2003年四川省开始开展全省汛期地质灾害气象预警预报工作,省级建立了区域性、警示性为特点的汛期地质灾害气象预警预报系统。2016年省级对地质灾害气象风险预警系统进行升级,优化了预警模型和细化预警预报单元网格,建立了地质灾害气象风险预警系统2.0版本,个别市县建立了自己的地质灾害气象风险预警系统。2019年起研究制定省市县三级地质灾害气象风险预警的指标和技术方法,在2022年汛前建立了四川省地质灾害气象风险预警互联系统3.0版本,于2022年开始试运行,2023年正式运行,省市县三级均可使用。
在预警机制方面,2003年,四川省建立《地质灾害气象预警预报工作业务规程》。2010年,建立《地质灾害防御会商、信息共享管理办法》,制订会商和信息共享细则,省级设立专人从事预警预报和值班值守工作,市、县级预警值班制度参照省级相继建立。2013年,建立《地质灾害防御会商、信息共享工作规程》,全面共享气象站点实时雨量资料。2022年建立了《四川省地质灾害分级预警工作导则(试行)》《四川省地质灾害监测预警响应指引(试行)》,构建了全省“平台共用、预警共商、分级负责、逐级精细”的地质灾害等级预报机制,从此开启了四川省地质灾害气象风险预警“省市县一体化”运行模式。每年汛期,四川省21个市(州)、175个地灾易发县均运用该系统平台,制定并发布地质灾害气象风险预警产品,指导基层组织避险转移工作。
4.2 预警成果
自2023年四川省地质灾害气象风险预警“省市县一体化”运行模型正式开展以来,全省累计发布黄色及以上预警6273次,发送预警短信747万条。其中:省级发布黄色预警97次,橙色预警8次,发送预警短信71万条;市(州)级发布黄色预警739次、橙色预警185次、红色预警10次,发送预警短信139万条;县(市、区)级发布黄色预警4437次、橙色预警750次,红色预警47次,发送预警短信536万条。2023年四川省实现成功避险案例28起,地质灾害气象风险预警发挥关键作用的有24起,占86%,充分发挥了地质灾害气象风险预警“作战图”作用。
4.3 实例分析
2023年7月26日16:00时省气象台发布气象预报,预计7月26日20时—7月27日20时川东大部地区有大到暴雨局部大暴雨,川南中雨为主,局部大到暴雨(图7),经互联系统模型分析计算预警区域见图8,与相关部门会商研判最终修正预警产品见图9。预警完成后,收集灾情情况,分析发现7月26日20时—7月27日20时四川省共发生3起灾情,其中2处位于预警区内,由于预警及时2处都实现了成功避险。其一:27日凌晨3时32分,凉山州会东县溜姑乡盘龙村发生泥石流灾害,造成6户房屋及20余亩农田受损,直接经济损失约20万元,因预警及时,提前转移全部受威胁群众3户10人(另外3户8人长期在外务工),避免了3户10人可能的因灾伤亡。其二:27日20时,绵阳市三台县郪江镇鱼洞村8组发生崩塌灾害,造成1户农房受损,直接经济损失3万元,因预警及时,提前转移受威胁群众1户2人,避免了1户2人可能的因灾伤亡。
5. 结论与展望
(1)预警模型方面,从单一的预警模型逐步建立预警模型库,对全省预警区划研究更加深入细致,预警精度逐级精细。在预警产品精度上,省级预警到市,精度到县;市级预警到县,精度到乡;县级预警到乡、村、点,精度到村。
(2)四川省基于互联网一体化统一平台研发实现了第三代地质灾害气象风险预警系统,是基于互联网的一体化统一平台,从技术上实现了省市县三级预警互联互通,解决了市县缺平台、缺经费、缺技术的三缺问题,实现集约化发展。
(3)四川省市县分级制作发布预警产品工作机制的建立,在气象风险预警工作机制上实现了探索与创新,为其他省份提供了示范与经验。在预警信息发布上,避免多头发布给基层防灾工作造成困扰的问题。2023年四川省成功避险的案例中气象风险预警发挥作用的占86%,有效减少了地质灾害造成的人员伤亡和财产损失。
地质灾害气象风险预警工作虽已经过20年的探索与实践,面对目前防灾形势及防灾能力需求,还有诸多问题需要解决,例如降雨预报准确率、模型研究程度、预报人员的技术水平、灾害反馈程度等。在下一步工作中四川省将选取试点开展基于深度学习的多源数据地质灾害气象风险预警模型研究,同时,进一步加强预警校验分析研究,突破行政与技术的壁垒,多行业全面收集地质灾害用于预警模型的优化与校验研究。
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表 1 各模型评价结果分区占比表
Table 1 Proportion of Landslide Zone Assessment Results for Each Model
极高/% 高/% 中/% 低/% LR 26.36 23.98 24.29 25.37 SVM 16.53 19.39 29.34 34.74 MLP 12.24 36.61 33.04 18.11 RF 20.16 25.58 20.75 33.51 -
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