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  

Evaluation of regional landslide susceptibility based on convolutional neural network:a case study of Wanzhou district of Three Gorges Reservoir area

作　　者：杨延晨 周超[2] 施佳湄 YANG Yanchen;ZHOU Chao;SHI Jiamei(S.K.Lee Honors College,China University of Geosciences,Wuhan 430074,China;School of Geography and Information Engineering,China University of Geosciences,Wuhan 430078,China)

机构地区：[1]中国地质大学(武汉)李四光学院,湖北武汉430074 [2]中国地质大学(武汉)地理与信息工程学院,湖北武汉430078

出　　处：《测绘通报》2023年第11期1-6,共6页Bulletin of Surveying and Mapping

基　　金：国家自然科学基金(42371094;41702330)。

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。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.

关 键 词：水库滑坡 易发性制图 卷积神经网络 三峡库区 

分 类 号：P208[天文地球—地图制图学与地理信息工程]