基于信息量与神经网络模型的滑坡易发性评价

doi:10.13722/j.cnki.jrme.2019.1094

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (6888 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要针对神经网络模型进行滑坡易发性评价时，传统的随机选取非滑坡单元存在准确性不高的缺点，提出信息量与神经网络结合的易发性评价模型。以江西省上犹县为研究区，首先，基于上犹县滑坡编录与实际调查，选取坡度、高程、坡向、平面曲率、剖面曲率，植被指数(NDVI)、湿度指数(TWI)、距水系距离、距道路距离、土地利用等10个环境因子，其次利用信息量模型对上犹县进行易发性分区，得到上犹县易发性分区图。然后，从信息量模型得出的易发性分区中的低易发区选取非滑坡单元，与滑坡编录中的历史滑坡点组成测试集与训练集，输入神经网络中训练模型，再将上犹县所有栅格输入，预测上犹县栅格的滑坡概率。最后利用自然断点法在上犹县栅格滑坡概率进行分类，得到基于信息量与人工神经网络结合的上犹县易发性分区图。由易发性结果表明：单独的信息量模型的成功率曲线下面积AUC=0.7364，历史灾害点位于高易发区与较高易发区的灾害数占总灾害数的55.6%；基于信息量与神经网络模型的AUC=0.7874；历史灾害点位于高易发区与较高易发区的灾害数占总灾害数的85.8%。信息量–神经网络的评价模型比单独的信息量模型的评价精度提高了5.1%；高易发区与较高易发区所涵盖的灾害数占比高30.2%。信息量–神经网络模型有更好的评价精度，并且证明了在信息量模型中的极低易发区选取非滑坡点具有可行性。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	陈飞1
	2
	蔡超1
	李小双1
	2
	孙涛1
	2
	钱乾1

关键词 ：边坡工程, 信息量模型, 神经网络, 滑坡易发性, 非滑坡单元

Abstract：The traditional method of randomly selecting non-slide units has the disadvantage of low accuracy when the evaluation of landslide susceptibility is carried out only based on neural network model. Thus，this paper proposes a susceptibility evaluation model that combines information value with neural network. Taking Shangyou County as a case study area. Firstly，ten environmental factors including slope，elevation，aspect，plane curvature，profile curvature，vegetation index(NDVI)，topographic wetness index(TWI)，distance to stream，distance to road and land use were employed to perform regional landslide susceptibility evaluation according landslide catalog and actual survey in the study area. Secondly，the susceptibility zoning of Shangyou County was carried out with the information value model，and the susceptibility zoning map of Shangyou County was obtained. Then，the non-landslide units were selected from the low-prone area in the susceptibility zone obtained from the information value model，and the test and training sets with the historical landslide points in the landslide catalog were divided. All the grids of the study area were then input into the resulting models to predict the landslide probability. Finally，the natural breakpoint method was used to classify the probability often grids，and a zoning map of landslides susceptibility based on the combination of information value and artificial neural network was obtained. The susceptibility results show that：the area under the success rate curve of the independent information model is AUC = 0.736 4，and the number of historical disaster points located in high-susceptibility areas and higher-susceptibility areas accounts for 55.6% of the total number of disasters；In comparison，the combined model based on both information value and neural network achieved a AUC value of 0.787 4，and the number of disasters located in the high-prone areas accounted for 85.8% of the total disasters. The evaluation accuracy of the information value-neural network is 5.1% higher than that of the information value model，and the number of disasters covered by high-prone areas is 30.2% higher than that of information value model. This implies that the information value-neural network model has better evaluation accuracy. It is verified that selecting non-slide points in extremely low-prone areas derived from the information value model is feasible for landslide susceptibility modelling.

Key words： slope engineering information model neural network landslide susceptibility non-landslide unit

引用本文:

陈飞1，2，蔡超1，李小双1，2，孙涛1，2，钱乾1. 基于信息量与神经网络模型的滑坡易发性评价[J]. 岩石力学与工程学报, 2020, 39(S1): 2859-2870.
CHEN Fei1，2，CAI Chao1，LI Xiaoshuang1，2，SUNTao1，2，QIAN Qian1. Evaluation of landslide susceptibility based on information volume and neural network model. , 2020, 39(S1): 2859-2870.

链接本文:

http://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2019.1094 或 http://rockmech.whrsm.ac.cn/CN/Y2020/V39/IS1/2859

[1]	殷坤龙，朱良峰. 滑坡灾害空间区划及GIS应用研究[J]. 地学前缘，2001，8(2)：279–284.(YIN Kunlong，ZHU Liangfeng. Landslide hazard zonation and application of GIS[J]. Earth Science Frontiers，2001，8(2)：279–284.(in Chinese))
[2]	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 support vector machine[J]. Chinese Journal of Rock Mechanics and Engineering，2018，37(1)：156–167.(in Chinese))
[5]	刘磊，殷坤龙，王佳佳，等. 降雨影响下的区域滑坡危险性动态评价研究——以三峡库区万州主城区为例[J]. 岩石力学与工程学报，2016，35(3)：558–569.(LIU Lei，YIN Kunlong，WANG Jiajia，et al. Dynamic evaluation of regional landslide hazard due to rainfall：a case study in Wanzhou central district，Three Gorges Reservoir[J]. Chinese Journal of Rock Mechanics and Engineering，2016，35(3)：558–569.(in Chinese))
[7]	CHEN W，POURGHASEMI H R，KORNEJADY A，et al. Landslide spatial modeling：introducing new ensembles of ANN，MaxEnt，and SVM machine learning techniques[J]. Geoderma，2017，305：314–327.
[4]	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))
[9]	BUI D T，TUAN T A，KLEMPE H，et al. Spatial prediction models for shallow landslide hazards：a comparative assessment of the efficacy of support vector machines，artificial neural networks，kernel logistic regression，and logistic model tree[J]. Landslides，2016，13(2)：361–378.
[12]	庄育龙，田原，程楚云. 基于深度神经网络的滑坡危险性评价——以深圳市为例[J]. 地理与地理信息科学，2019，35(2)：104–110.(ZHUANG Yulong，TIAN Yuan，CHENG Chuyun. Evaluation of landslide hazard based on deep neural network——A case study of Shenzhen City[J]. Geography and Geo-information Science，2019，35(2)：104–110.(in Chinese))
[3]	ALTHUWAYNEE O F，PRADHAN B，PARK H J，et al. A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping[J]. Catena，2014，114：21–36.
[19]	范林峰，胡瑞林，曾逢春，等. 加权信息量模型在滑坡易发性评价中的应用——以湖北省恩施市为例[J]. 工程地质学报，2012，20(4)：508–513.(FAN Linfeng，HU Ruilin，ZENG Fengchun et al. Application of weighted information model in landslide susceptibility evaluation——Taking Enshi City，Hubei Province as an example[J]. Journal of Engineering Geology，2012，20(4)：508–513.(in Chinese))
[6]	刘磊. 三峡水库万州区库岸滑坡灾害风险评价研究[博士学位论文][D]. 武汉：中国地质大学，2016.(LIU Lei. Risk assessment of reservoir bank landslide disaster in Wanzhou district of Three Gorges Reservoir[Ph. D. Thesis][D]. Wuhan：China University of Geosciences，2016.(in Chinese))
[22]	杜国梁，张永双，吕文明等. 基于加权信息量模型的藏东南地区滑坡易发性评价[J]. 灾害学，2016，31(2)：226–234.(DU Guoliang，ZHANG Yongshuang，LU Wenming et al. Evaluation of landslide susceptibility based on weighted information model in southeastern Tibet[J]. Journal of Catastrophology，2016，31(02)：226–234.(in Chinese))
[8]	ZHOU C，YIN K L，CAO Y，et al. Application of time series analysis and PSO-SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir，China[J]. Engineering Geology，2016，204：108–120.
[10]	吴孝情，赖成光，陈晓宏，等. 基于随机森林权重的滑坡危险性评价：以东江流域为例[J]. 自然灾害学报，2017，26(5)：119–129.(WU Xiaoqing，LAI Chengguang，CHEN Xiaohong，et al. Risk assessment of landslide based on random forest weight：taking Changjiang River Basin as an example[J]. Journal of Natural Disasters，2017，26(5)：119–129.(in Chinese))
[11]	PARK I，LEE S. Spatial prediction of landslide susceptibility using a decision tree approach：a case study of the Pyeongchang area，Korea[J]. International Journal of Remote Sensing，2014，35(16)：6 089–6 112.
[14]	HANG H，PRADHAN B，XU C，et al. Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression，alternating decision tree and support vector machines[J]. Catena，2015，133：266–281.
[16]	NEFESLIOGLU H A，GOKCEOGLU C，SONMEZ H. An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps[J]. Engineering Geology，2008，97(3/4)：171–191.
[18]	KAVZOGLU T，SAHIN E K，COLKESEN I. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis，support vector machines，and logistic regression[J]. Landslides，2014，11(3)：425–439.
[20]	谈树成，赵娟娟，杨林，等. 基于GIS和信息量–快速聚类模型的滑坡易发性研究——以云南省福贡县为例[J]. 云南大学学报：自然科学版，2018，40(6)：1 148–1 158.(TAN Shucheng，ZHAO Juanjuan，YANG Lin，et al. Study on landslide susceptibility based on GIS and information-fast clustering model——A case study of Fugong County，Yunnan Province[J]. Journal of Yunnan University：Natural Science，2018，40(6)：1 148–1 158.(in Chinese))
[21]	王佳佳，殷坤龙，肖莉丽. 基于GIS和信息量的滑坡灾害易发性评价——以三峡库区万州区为例[J]. 岩石力学与工程学报，2014，33(4)：797–808.(WANG Jiajia，YIN Kunlong，XIAO Lili. Landslide susceptibility assessment based on GIS and weighted information value：a case study of Wanzhou district，Three Gorges Reservoir[J]. Chinese Journal Rock Mechanics and Engineering，2014，33(4)：797–808.(in Chinese))
[24]	MINGOTI S A，LIMA J O. Comparing SOM neural network with Fuzzy c-means，K-means and traditional hierarchical clustering algorithms[J]. European journal of operational research，2006，174(3)：1 742–1 759.
[13]	夏辉，殷坤龙，梁鑫，等. 基于SVM-ANN模型的滑坡易发性评价——以三峡库区巫山县为例[J]. 中国地质灾害与防治学报，2018，29(5)：13–19.( XIA Hui，YIN Kunlong，LIANG Xin，et al. Evaluation of landslide susceptibility based on SVM-ANN model——Taking Wushan County in the Three Gorges Reservoir Area as an example[J]. Chinese Journal of Geological Hazard and Control，2018，29(5)：13–19.(in Chinese))
[15]	王丽丽，苏程，冯存均，等. 数据驱动自适应更新的斜坡地质灾害易发性评价系统[J].岩石力学与工程学报，2016，35(增1)：3 076–3 083.(WANG Lili，SU Cheng，FENG Cunjun，et al. A data driven self-adaptive update landslide susceptibility assessment system[J]. Chinese Journal of Rock Mechanics and Engineering，2016，35(Supp.1)：3 076–3 083.(in Chinese))
[17]	戴福初，姚鑫，谭国焕. 滑坡灾害空间预测支持向量机模型及其应用[J]. 地学前缘，2007，14(6)：153–159.(DAI Fuchu，YAOXin，TAN Guohuan. Landslide susceptibility mapping using support vector machines[J]. Earth Science Frontiers，2007，14(6)：153–159.(in Chinese))
[23]	谭玉敏，郭栋，白冰心，等. 基于信息量模型的涪陵区地质灾害易发性评价[J]. 地球信息科学学报，2015，17(12)：1 554–1 562. (TAN Yumin，GUO Dong，BAI Bingxin，et al. Evaluation of geological hazard susceptibility in Fuling District based on information model[J]. Journal of Geo-Information Science，2015，17(12)：1 554–1 562.(in Chinese))