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

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 specific models,indicating no factors commonly suitable for all five models.3)The RF62 model was achieved with the highest prediction accuracy.The AUC value,sensitivity,specificity,and accuracy of the RF62 model were 0.96,0.83,0.93,and 0.86,respectively.The second precision model was ANN53,where the AUC value,sensitivity,specificity,and accuracy were 0.926,0.80,0.79,and 0.79,respectively.The third prediction accuracy was the support vector machine model,where the AUC value,sensitivity,specificity,and accuracy were 0.90,0.82,0.73,and 0.77,respectively.The fourth prediction accuracy was LOGIT325,where the AUC value,sensitivity,specificity,and accuracy were 0.876,0.83,0.72,and 0.77,respectively.The worst accuracy was obtained from the GAM597 model,where the AUC value,sensitivity,specificity,and accuracy were 0.87,0.82,0.73,and 0.77,respectively.4)The RF model performed the most accurate to predict the landslides,with 95.05%accuracy and coverage of 25.31 km^(2)within the highly susceptible areas;artificial neural network model,the support vector machine model,generalized additive model,and logistic regression were followed with 78.57%,69.78%,68.13%,and 67.58%accuracy and coverage of 35.43,22.02,26.26 and 26.27 km^(2)within the highly susceptible areas,respectively;5)The primary vegetation with shallow landslides was characterized by the low density(1000-1500 plants/hm^(2)),high storage volume(>80m3/hm^(2)),and advanced age(>30 a).The findings can provide scientific decision-making and technical assistance for early warning,prevention,and control of rainstorm-induced landslides in high vegetation cover areas of China.