基于卷积神经网络的区域滑坡易发性评价：以三峡库区万州区为例

doi:10.13474/j.cnki.11-2246.2023.0318

摘要/Abstract

摘要： 开展区域滑坡易发性评价是滑坡气象预警与风险评价的关键。针对目前诸多易发性研究未考虑滑坡发生与邻接环境有关的情况,本文提出了一种基于卷积神经网络(CNN)的区域滑坡易发性建模框架。以三峡库区万州区为例,选取坡度、坡向等12个因子构建评价指标体系,通过信息量法分析因子对滑坡发育的影响程度,采用二维矩阵构建数据集,运用CNN进行易发性建模,得到易发性评价图,同时探究构建样本时二维矩阵的大小对精度的影响。研究结果表明,越靠近水库带越易发生滑坡,水系和人类工程活动对于滑坡发育具有较大影响;CNN模型精度为0.925,相比机器学习模型精度明显提升;增大构建样本时的二维矩阵可提高精度。CNN模型在多维空间数据处理方面具有优势,它考虑了滑坡位置及其邻接环境的影响,是一种准确可靠的区域滑坡易发性评价方法。

关键词: 水库滑坡, 易发性制图, 卷积神经网络, 三峡库区

Abstract: Carrying out regional landslide susceptibility assessment is the key to landslide meteorological early warning and risk assessment. Aiming at the fact that many current susceptibility studies do not consider the relationship between the occurrence of landslides and adjacent environments, a regional landslide susceptibility modeling framework based on convolutional neural network (CNN) is proposed. Taking Wanzhou district of the Three Gorges Reservoir area as an example, 12 factors such as slope and aspect are selected to construct an evaluation index system, and the influence of factors on landslide development is analyzed by information method. The local two-dimensional matrix is used to construct the dataset, CNN is used for susceptibility modeling. At the same time, the impact of the size of the local two-dimensional matrix to the accuracy when constructing samples is explored. The results show that landslides are more likely to occur the closer to the reservoir zone, and the water system and human engineering activities have a greater impact on the development of landslides. The accuracy of the CNN model is 0.925, which is significantly higher than that of the machine learning model, and the accuracy can be improved by increasing the local two-dimensional matrix size when constructing the sample. The CNN model has advantages in multidimensional spatial data processing, considering the influence of landslide location and its adjacent environment, and it is an accurate and reliable regional landslide susceptibility evaluation method.

Key words: reservoir landslide, susceptibility mapping, convolutional neural network, Three Gorges Reservoir area

中图分类号:

P208

杨延晨, 周超, 施佳湄. 基于卷积神经网络的区域滑坡易发性评价：以三峡库区万州区为例[J]. 测绘通报, 2023, 0(11): 1-6.

YANG Yanchen, ZHOU Chao, SHI Jiamei. Evaluation of regional landslide susceptibility based on convolutional neural network: a case study of Wanzhou district of Three Gorges Reservoir area[J]. Bulletin of Surveying and Mapping, 2023, 0(11): 1-6.

参考文献

[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.
[2] 李媛,孟晖,董颖,等. 中国地质灾害类型及其特征: 基于全国县市地质灾害调查成果分析[J]. 中国地质灾害与防治学报,2004,15(2): 29-34.
[3] ZHANG Huijuan,SONG Yingxu,XU Shiluo,et al. Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: a case study of Wanzhou section of the Three Gorges Reservoir,China[J]. Computers & Geosciences,2022,158: 104966.
[4] 周剑,邓茂林,李卓骏,等. 三峡库区杉树槽滑坡成因及视向滑动机制分析[J]. 防灾减灾工程学报,2020,40(6): 860-866.
[5] 桂蕾,殷坤龙,王佳佳. 基于聚类分析的滑坡灾害危险性区划研究[J]. 水文地质工程地质,2013,40(1): 100-105.
[6] KALANTAR B,PRADHAN B,NAGHIBI S A,et al. Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM),logistic regression (LR) and artificial neural networks (ANN)[J]. Geomatics,Natural Hazards and Risk,2018,9:49-69.
[7] SVZEN M L,DOYURAN V. A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate[J]. Environmental Geology,2004,45(5): 665-679.
[8] CHEN Wei,LI Wenping,HOU Enke,et al. Landslide susceptibility mapping based on GIS and information value model for the Chencang District of Baoji,China[J]. Arabian Journal of Geosciences,2014,7(11): 4499-4511.
[9] CHEN Tao,NIU Ruiqing,JIA Xiuping. A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS[J]. Environmental Earth Sciences,2016,75(10): 1-16.
[10] BAEZA C,COROMINAS J. Assessment of shallow landslide susceptibility by means of multivariate statistical techniques[J]. Earth Surtace Processes and Landforms,2001,26(12): 1251-1263.
[11] 周超,殷坤龙,曹颖,等. 基于集成学习与径向基神经网络耦合模型的三峡库区滑坡易发性评价[J]. 地球科学,2020,45(6): 1865-1876.
[12] ZHOU Chao,CAO Ying,PERES D J,et al. Editorial: advancement in quantitative risk analysis of geological disaster in reservoir areas[J]. Frontiers in Earth Science,2023,11: 1148194.
[13] 周超,甘露露,王悦,等. 综合非滑坡样本选取指数与异质集成机器学习的区域滑坡易发性建模[J]. 地球信息科学学报,2023,25(8): 1570-1585.
[14] REGMI A D,DEVKOTA K C,YOSHIDA K,et al. Application of frequency ratio,statistical index,and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya[J]. Arabian Journal of Geosciences,2014,7(2): 725-742.
[15] DEVKOTA K C,REGMI A D,POURGHASEMI H R,et al. Landslide susceptibility mapping using certainty factor,index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat Road section in Nepal Himalaya[J]. Natural Hazards,2013,65(1): 135-165.
[16] 牛瑞卿,彭令,叶润青,等. 基于粗糙集的支持向量机滑坡易发性评价[J]. 吉林大学学报(地球科学版),2012,42(2): 430-439.
[17] 乔德京,王念秦,郭有金,等. 加权确定性系数模型的滑坡易发性评价[J]. 西安科技大学学报,2020,40(2): 259-267.
[18] AZARAFZA M,AZARAFZA M,AKGVN H,et al. Deep learning-based landslide susceptibility mapping[J]. Scientific Reports,2021,11: 24112.
[19] 王毅,方志策,牛瑞卿,等. 基于深度学习的滑坡灾害易发性分析[J]. 地球信息科学学报,2021,23(12): 2244-2260.
[20] 王斌,范冬林. 深度学习在遥感影像分类与识别中的研究进展综述[J]. 测绘通报,2019(2): 99-102.
[21] 陶翊婷. 基于深度学习的高空间分辨率遥感影像分类方法研究[D]. 武汉: 武汉大学,2019.
[22] 施慧慧,徐雁南,滕文秀,等. 高分辨率遥感影像深度迁移可变形卷积的场景分类法[J]. 测绘学报,2021,50(5): 652-663.
[23] 黄武彪,丁明涛,王栋,等. 基于层数自适应加权卷积神经网络的川藏交通廊道沿线滑坡易发性评价[J]. 地球科学,2022,47(6): 2015-2030.
[24] CHEN Zhiqiang,LI Chuan,SANCHEZ R V. Gearbox fault identification and classification with convolutional neural networks[J]. Shock and Vibration,2015,2015: 1-10.
[25] SHAHABI H,HASHIM M. Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment[J]. Scientific Reports,2015,5: 9899.
[26] YU Lanbing,ZHOU Chao,WANG Yang,et al. Coupling data-and knowledge-driven methods for landslide susceptibility mapping in human-modified environments: a case study from Wanzhou County,Three Gorges Reservoir area,China[J]. Remote Sensing,2022,14(3): 774.
[27] GU Jiuxiang,WANG Zhenhua,KUEN J,et al. Recent advances in convolutional neural networks[J]. Pattern Recognition,2018,77: 354-377.
[28] FAWCETT T. An introduction to ROC analysis[J]. Pattern Recognition Letters,2006,27(8): 861-874.
[29] ZHOU Chao,CAO Ying,YIN Kunlong,et al. Characteristic comparison of seepage-driven and buoyancy-driven landslides in Three Gorges Reservoir area,China[J]. Engineering Geology,2022,301: 106590.
[30] 杨秀元,付杰,韩旭东,等. 三峡库区沿江城镇地质灾害风险评价: 以重庆市万州区大周镇为例[J]. 中国地质调查,2022,9(4): 27-36.
[31] ZHOU Chao,CAO Ying,HU Xie,et al. Enhanced dynamic landslide hazard mapping using MT-InSAR method in the Three Gorges Reservoir Area[J]. Landslides,2022,19(7): 1585-1597.
[32] 贺鹏,童立强,郭兆成,等. GIS支持下基于层次分析法的西藏札达地区滑坡灾害易发性评价研究[J]. 科学技术与工程,2016,16(25): 193-200.
[33] 周超,殷坤龙,向章波,等. 基于GIS的淳安县滑坡易发性定量评价[J]. 安全与环境工程,2015,22(1): 45-50.
[34] CAO Ying,YIN Kunlong,ZHOU Chao,et al. Establishment of landslide groundwater level prediction model based on GA-SVM and influencing factor analysis[J]. Sensors,2020,20(3): 845.