不同空间分辨率和训练测试集比例下的滑坡易发性预测不确定性

doi:10.13722/j.cnki.jrme.2020.1119

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摘要探讨空间分辨率和模型训练测试集比例等因素对滑坡易发性预测不确定性的影响具有重要意义。拟以江西省宁都县的滑坡为例，首先计算各环境因子在不同空间分辨率(15，30，60，90和120 m)下的频率比值；然后将滑坡和非滑坡样本按照9/1，8/2，7/3，6/4和5/5的比例划分出不同的模型训练和测试数据集，由此得到25种组合工况下的模型输入和输出变量；再将其导入支持向量机(support vector machine，SVM)和随机森林(random forest，RF)模型中开展滑坡易发性预测，最后利用预测精度和易发性指数特征来分析各工况下的易发性建模不确定性。结果表明：(1) 15 m分辨率、9∶1训练测试集比例和RF模型工况下的易发性预测精度最高，各工况下重要的环境因子分别为高程、坡度和地形起伏度等；(2) 随着分辨率的下降或训练测试集比例的减小，SVM和RF模型的预测精度均逐渐下降，易发性指数的均值变大且标准差减小；(3) 所有工况下随着分辨率和训练测试集比例的减小，易发性预测精度均逐渐下降且其不确定性增大；(4) 各工况下RF预测精度优于SVM，分辨率对RF模型的影响显著大于训练测试集比例，而在SVM中2种因素的影响相差不大。

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	黄发明1
	2
	陈佳武1
	唐志鹏1
	范宣梅2
	黄劲松3
	周创兵1
	常志璐1

关键词 ：边坡工程, 滑坡易发性, 不确定性分析, 机器学习, 环境因子, 空间分辨率, 训练测试集比例

Abstract：It is of great significance to explore the influences of the spatial resolution and the proportion of model training and testing dataset on the uncertainties of landslide susceptibility prediction(LSP). Taking the landslides in Ningdu County of Jiangxi Province as examples，the frequency ratios of various environmental factors under different spatial resolutions(15，30，60，90 and 120 m) are firstly calculated. Then，the landslide and non-landslide samples are divided into different model training and testing datasets with the proportions of 9/1，8/2，7/3，6/4 and 5/5，and the model input and output variables under 25 combined conditions are obtained. Furthermore，these input and output variables are imported into the Support Vector Machine(SVM) and Random Forest(RF) models to carry out LSP. Finally，the uncertainties of LSP modeling under the 25 combined conditions are discussed using the accuracy assessment as well as the distribution characteristics of landslide susceptibility indexes. The results show that the landslide susceptibility accuracy predicted by the RF model under the spatial resolution of 15 m and training and testing dataset proportion of 9∶1 is the highest，and that the more important environmental factors under each condition are elevation，slope and topographic relief，etc. With decreasing the spatial resolution and/or the proportion of training and testing dataset，the LSP accuracies of both SVM and RF models decrease gradually，and the mean values of landslide susceptibility indexes increase with a decrease of the corresponding standard deviation value. For all combined conditions，as the spatial resolutions and the proportions of training and testing dataset decrease，the LSP accuracies decrease gradually while the corresponding uncertainties increase. It is also indicated that the LSP accuracy of the RF model is better than that of the SVM model under various combined conditions，and that the influence of the spatial resolution on the RF model is significantly greater than that of the proportion of training and testing dataset while there is little difference between the effects of the two factors on the SVM model.

Key words： slope engineering landslide susceptibility prediction uncertainty analysis machine learning environmental factors spatial resolution proportions of training and testing datasets

引用本文:

黄发明1，2，陈佳武1，唐志鹏1，范宣梅2，黄劲松3，周创兵1，常志璐1. 不同空间分辨率和训练测试集比例下的滑坡易发性预测不确定性[J]. 岩石力学与工程学报, 2021, 40(6): 1155-1169.
HUANG Faming1，2，CHEN Jiawu1，TANG Zhipeng1，FAN Xuanmei2，HUANG Jinsong3，ZHOU Chuangbing1，CHANG Zhilu1. Uncertainties of landslide susceptibility prediction due to different spatial resolutions and different proportions of training and testing datasets. , 2021, 40(6): 1155-1169.

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https://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2020.1119 或 https://rockmech.whrsm.ac.cn/CN/Y2021/V40/I6/1155

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