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

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (14191 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

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

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	HUANG Faming1
	2
	CHEN Jiawu1
	TANG Zhipeng1
	FAN Xuanmei2
	HUANG Jinsong3
	ZHOU Chuangbing1
	CHANG Zhilu1

Cite this article:

HUANG Faming1,2,CHEN Jiawu1, et al. Uncertainties of landslide susceptibility prediction due to different spatial resolutions and different proportions of training and testing datasets[J]. , 2021, 40(6): 1155-1169.

URL:

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

[1]	张俊，殷坤龙，王佳佳，等. 三峡库区万州区滑坡灾害易发性评价研究[J]. 岩石力学与工程学报，2016，35(2)：284-296.(ZHANG Jun，YIN Kunlong，WANG Jiajia，et al. Evaluation of landslide susceptibility for Wanzhou district of Three Gorges Reservoir[J]. Chinese Journal of Rock Mechanics and Engineering，2016，35(2)：284-296.(in Chinese))
[2]	郭子正，殷坤龙，唐扬，等. 库水位下降及降雨作用下麻柳林滑坡稳定性评价与预测[J]. 地质科技情报，2017，36(4)：260-265. (GUO Zizheng，YIN Kunlong，TANG Yang，et al. Stability evaluation and presiction of Maliulin landslide under reservoir water level decline and rainfall[J]. Geological Science and Technology Information，2017，36(4)：260-265.(in Chinese))
[3]	CHEN Z，YE F，FU W，et al. The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin，NW China[J]. Natural Hazards，2020，101(3)：853-877.
[4]	ZHAO Y，WANG R，JIANG Y，et al. GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing，Southeastern China[J]. Engineering Geology，2019，259：105147.
[5]	黄发明，叶舟，姚池，等. 滑坡易发性预测不确定性：环境因子不同属性区间划分和不同数据驱动模型的影响[J]. 地球科学，2020，45(12)：4 535-4 549.(HUNAG Faming，YE Zhou，YAO Chi，et al. Uncertainties of landslide susceptibility prediction：different attribute interval divisions of environmental factors and different data?based models[J]. Earth Science，2020，45(12)：4 535-4 549.(in Chinese))
[6]	SHIRZADI A，SOLAIMANI K，ROSHAN M H，et al. Uncertainties of prediction accuracy in shallow landslide modeling：Sample size and raster resolution[J]. Catena，2019，178：172-188.
[7]	YANG P，AMES D P，FONSECA A，et al. What is the effect of LiDAR-derived DEM resolution on large-scale watershed model results?[J]. Environmental Modelling and Software，2014，58：48-57.
[8]	IWAHASHI J，KAMIYA I，YAMAGISHI H. High-resolution DEMs in the study of rainfall- and earthquake-induced landslides：Use of a variable window size method in digital terrain analysis[J]. Geomorphology，2012，153-154：29-38.
[9]	ARNONE E，FRANCIPANE A，SCARBACI A，et al. Effect of raster resolution and polygon-conversion algorithm on landslide susceptibility mapping[J]. Environmental Modelling and Software，2016，84：467-481.
[10]	CAVAZZI S，CORSTANJE R，MAYR T，et al. Are fine resolution digital elevation models always the best choice in digital soil mapping[J]. Geoderma，2013，195(1)：111-121.
[11]	GAROSI Y，SHEKLABADI M，POURGHASEMI H R，et al. Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping[J]. Geoderma，2018，330：65-78.
[12]	CAMA M，CONOSCENTI C，LOMBARDO L，et al. Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models：a test in the Giampilieri catchment(Sicily，Italy)[J]. Environmental Earth Sciences，2016，75(3)：1-21.
[13]	LUCÀ F，CONFORTI M，ROBUSTELLI G. Comparison of GIS-based gullying susceptibility mapping using bivariate and multivariate statistics：Northern Calabria，South Italy[J]. Geomorphology，2011，134(3-4)：297-308.
[14]	李军，周成虎. 基于栅格GIS滑坡风险评价方法中格网大小选取分析[J]. 遥感学报，2003，7(2)：86-92.(LI Jun，ZHOU Chenghu. Appropriate grid size for terrain based landslide risk assessment in Lantau Island，Hong Kon[J]. Journal of Remote Sensing，2003，7(2)：86-92.(in Chinese))
[15]	范林峰，胡瑞林，周顺江，等. 地质灾害危险性评价因子对格网大小的敏感性与误差分析[J]. 工程地质学报，2012，20(2)：152-159. (FAN Linfeng，HU Ruilin，ZHOU Shunjiang，et al. Sensitivity and error analysis of controlling factors due to variation of grid sizes in geohazard assessment[J]. Journal of Engineering Geology，2012，20(2)：152-159.(in Chinese))
[16]	张玘恺，凌斯祥，李晓宁，等. 九寨沟县滑坡灾害易发性快速评估模型对比研究[J]. 岩石力学与工程学报，2020，39(8)：1 595-1 610. (ZHANG Qikai，LING Sixiang，LI Xiaoning，et al. Comparison of landslide susceptibility mapping rapid assessment models in Jiuzhaigou County，Sichuan province，China[J]. Chinese Journal of Rock Mechanics and Engineering，2020，39(8)：1 595-1 610.(in Chinese))
[17]	HUANG F，CAO Z，GUO J，et al. Comparisons of heuristic，general statistical and machine learning models for landslide susceptibility prediction and mapping[J]. CATENA，2020，191：104580.
[18]	REICHENBACH P，ROSSI M，MALAMUD B D，et al. A review of statistically-based landslide susceptibility models[J]. Earth-Science Reviews，2018，180：60-91.
[19]	黄发明，殷坤龙，张桂荣，等. 基于相空间重构和小波分析-粒子群向量机的滑坡地下水位预测[J]. 地球科学：中国地质大学学报，2015，40(7)：1 254-1 265.(HUANG Faming，YIN Kunlong，ZHANG Guirong，et al. Landslide groundwater level time series prediction bases on phase space reconstruction and wavelet analysis-support vector machine optimized by pso algorithm[J]. Earth Science-Journal of China University of Geosciences，2015，40(7)：1 254-1 265.(in Chinese))
[20]	黄发明，殷坤龙，蒋水华，等. 基于聚类分析和支持向量机的滑坡易发性评价[J]. 岩石力学与工程学报，2018，37(1)：156-167. (HUANG Faming，YIN Kunlong，JIANG Shuihua，et al. Landslide susceptibility assessment based on clustering analysis and supportvector machine[J]. Chinese Journal of Rock Mechanics and Engineering，2018，37(1)：156-167.(in Chinese))
[21]	张书豪，吴光. 随机森林与GIS的泥石流易发性及可靠性[J]. 地球科学，2019，44(9)：3 115-3 134.(ZHANG Shuhao，WU Guang. Debris flow susceptibility and its reliability based on random forest and GIS[J]. Earth Science，2019，44(9)：3 115-3 134.(in Chinese))
[22]	刘坚，李树林，陈涛. 基于优化随机森林模型的滑坡易发性评价[J]. 武汉大学学报：信息科学版，2018，43(7)：1 085-1 091.(LIU Jian，LI Shulin，CHEN Tao. Landslide susceptibility assesment based on optimized random forest model[J]. Geomatics and Information Science of Wuhan University，2018，43(7)：1 085-1 091.(in Chinese))
[23]	许冲，戴福初，姚鑫，等. GIS支持下基于层次分析法的汶川地震区滑坡易发性评价[J]. 岩石力学与工程学报，2009，28(增2)：3 978-3 985.(XU Chong，DAI Fuchu，YAO Xin，et al. Gis-based landslide susceptibility assessment using analytical hierarchy process in Wenchuan earthquake region[J]. Chinese Journal of Rock Mechanics and Engineering，2009，28(Supp.2)：3 978-3 985.(in Chinese))
[24]	郭子正，殷坤龙，黄发明，等. 基于滑坡分类和加权频率比模型的滑坡易发性评价[J]. 岩石力学与工程学报，2019，38(2)：287-300. (GUO Zizheng，YIN Kunlong，HUANG Faming，et al. Evaluation of landslide susceptibility based on landslide classification and weighted frequency ratio model[J]. Chinese Journal of Rock Mechanics and Engineering，2019，38(2)：287-300.(in Chinese))
[25]	CHANG Z，DU Z，ZHANG F，et al. Landslide susceptibility prediction based on remote sensing images and GIS：comparisons of supervised and unsupervised machine learning models[J]. Remote Sensing，2020，12(3)：104580.
[26]	SEGONI S，PICIULLO L，GARIANO S L. A review of the recent literature on rainfall thresholds for landslide occurrence[J]. Landslides，2018，15(8)：1 483-1 501.
[27]	HUANG F，CAO Z，JIANG S H，et al. Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model[J]. Landslides，2020，17：2 919-2 930.
[28]	CHEN W，PENG J，HONG H，et al. Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County，Jiangxi Province，China[J]. Science of The Total Environment，2018，626：1 121-1 135.
[29]	HONG H，POURGHASEMI H R，POURTAGHI Z S. Landslide susceptibility assessment in Lianhua County(China)：A comparison between a random forest data mining technique and bivariate and multivariate statistical models[J]. Geomorphology，2016，259：105-118.
[30]	CHEN W，POURGHASEMI H R，PANAHI M，et al. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio，generalized additive model，and support vector machine techniques[J]. Geomorphology，2017，297：69-85.
[31]	ZHU L，HUANG L，FAN L，et al. Landslide susceptibility prediction modeling based on remote sensing and a novel deep learning algorithm of a cascade-parallel recurrent neural network[J]. Sensors (Basel)，2020，20(6)：1 576.
[32]	黄发明，汪洋，董志良，等. 基于灰色关联度模型的区域滑坡敏感性评价[J]. 地球科学，2019，44(2)：664-676.(HUNAG Faming，WANG Yang，DONG Zhiliang，et al. Regiional landslide susceptibility mapping based on grey relational degree model[J]. Earth Science，2019，44(2)：664-676.(in Chinese))
[33]	郭子正，殷坤龙，付圣，等. 基于GIS与WOE-BP模型的滑坡易发性评价[J]. 地球科学，2019，44(12)：4 299-4 312.(GUO Zizheng，YIN Kunlong，FU Sheng，et al. Evaluation of landslide susceptibility based on GIS and WOE-BP model[J]. Earth Science，2019，44(12)：4 299-4 312.(in Chinese))
[34]	LI D Y，HUANG F M，YAN L X，et al. Landslide susceptibility prediction using particle-swarm-optimized multilayer perceptron：comparisons with multilayer-perceptron-only，BP Neural Network，and information value models[J]. Applied Sciences，2019，9(18)：3 664.
[35]	TAROLLI P，TARBOTON D G. A new method for determination of most likely initiation points and the evaluation of digital terrain model scale in terrain stability mapping[J]. Hydrology and Earth System Sciences Discussions，2006，3(2)：395-425.
[36]	TIAN Y，XIAO C C，LIU Y，et al. Effects of raster resolution on landslide susceptibility mapping：A case study of Shenzhen[J]. Science in China Series E：Technological Sciences，2008，51(Supp.2)：188-198.
[37]	POGSON M，SMITH P. Effect of spatial data resolution on uncertainty[J]. Environmental Modelling and Software，2015，63：87-96.
[38]	TSANGARATOS P，ILIA I. Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments：The influence of models complexity and training dataset size[J]. CATENA，2016，145：164-179.
[39]	郭天颂，张菊清，韩煜，等. 基于粒子群优化支持向量机的延长县滑坡易发性评价[J]. 地质科技情报，2019，38(3)：236-243.(GUO Tiansong，ZHANG Juqing，HAN Yu，et al. Evaluation of landslide susceptibility in Yanchang County based on particle swarm optimization-based support vector machine[J]. Geological Science and Technology Information，2019，38(3)：236-243.(in Chinese))