基于机器学习模型的高植被覆盖区浅层滑坡预测  

Predicting shallow landslides in highly vegetation-covered areas using machine learning models

作　　者：张林[1] 郭郑曦 齐实[1] 伍冰晨 李鹏[1] ZHANG Lin;GUO Zhengxi;QI Shi;WU Bingchen;LI Peng(School of Water and Soil Conservation,Beijing Forestry University,Beijing 100083,China;Guangxi Communications Design Group Co.,Ltd,Nanning 530029,China;Jiangxi Academy of Water Science and Engineering,Nanchang 330029,China)

机构地区：[1]北京林业大学水土保持学院,北京100083 [2]广西交通设计集团有限公司,南宁530029 [3]江西省水利科学院,南昌330029

出　　处：《农业工程学报》2024年第17期149-160,共12页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(32271964);国家重点研发计划项目“川东山地灾害区森林生态系统服务提升技术研究与示范”(2017YFC0505602)。

摘　　要：为探究高植被覆盖区浅层滑坡的影响因素并构建最优的滑坡预测模型,该研究以华蓥市山区林地为研究对象,考虑了蓄积量、林分密度、平均树龄、林分类型和红绿植被指数(green-red vegetation index,GRVI)等植被因子,并结合地形地质因素,经过Boruta重要性分析以及共线性诊断,利用Logistic回归模型、广义相加模型、随机森林模型、支持向量机模型和人工神经网络模型等5种机器学习模型构建华蓥市山区林地浅层滑坡预测模型,并结合历史滑坡点检验,提出华蓥市山区林地浅层滑坡的最佳预测模型及高易发性区域的植被特征。结果表明:1)工程地质岩组、距河流距离、距断层距离、林分类型、平均树龄和蓄积量是影响浅层滑坡的主要因子;2)不同因子组合对模型精度有极大的影响,考虑蓄积量、林分密度、平均树龄等植被因子有利于提高模型的预测精度;3)在5种模型中,预测精度最高的模型为随机森林模型,精度可达到95.05%;4)研究区高易发性及以上区域的面积为25.31 km^(2),占研究区总面积的14.79%,低密度(1000~1500株/hm^(2))、高蓄积量(>80 m3/hm^(2))和高树龄(>30 a)是浅层滑坡发生的主要植被特征。该研究结果可为中国高植被覆盖区极端暴雨型滑坡的预警与防控提供科学决策和技术支撑。Extreme rainstorm-triggered shallow landslides can often occur in highly vegetation-covered areas,due mainly to the synergistic interactions among geological,vegetation,and meteorological factors.In this study,a landslide prediction model was constructed with high accuracy,in order to reveal the influence of vegetation factors on shallow landslides.Taking the mountain forest in Huaying city as study site,various vegetation factors were selected,including stock volume,stand density,average tree age,stand types,and green-red vegetation index(GRVI),and combined with topographic and geological factors(engineering geology rock group,distance to faults,distance to river,elevation,coefficient of elevation variation,slope,slope variation,relief degree of land surface,surface curvature,section curvature,aspect,and soil thickness).According to Boruta's importance and multicollinearity analysis,five kinds of shallow landslide prediction models were built using machine learning techniques,including the Logistic regression,Generalized additive model,random forest(RF),Support vector machine,and artificial neural network model.The prediction accuracy of the five models was evaluated by sensitivity,specificity,accuracy,and AUC values.Coupling with the previous records of landslide points,the prediction models were validated to determine the vegetation characteristics of high-risk areas in Huaying mountain forests.The research demonstrated that:1)The susceptibility of shallow landslides was primarily influenced by the engineering geological rock groups,distance from rivers,distance from faults,stand types,average age,and stock volume.There was relatively little influence of environmental and vegetation factors on the susceptibility of shallow landslides.2)The combination of different factors shared a great impact on the accuracy of the model,in terms of the vegetation factors(stand density,average age,and stock volume).The prediction accuracies of the five models were improved significantly;All factors were only used in the speci

关 键 词：植被 滑坡 边坡失稳 林地 机器学习模型 

分 类 号：P642.22[天文地球—工程地质学]