滑坡易发性评价是区域滑坡预警和评估工作的前期准备。滑坡致灾因子的有效选取以及评价模型的构建成为当前滑坡预测研究中的难点问题。以府谷县作为研究区,借助多种技术手段将数字高程模型(DEM)、地质图、路网图、遥感影像图等多源数据进行融合,提取了地形地貌、地层岩性以及地表覆盖等滑坡孕灾环境因子和降雨量、人类工程活动等诱发因素的特征属性作为评价指标。在此基础上,对提取的各因子的相关性进行分析,剔除了地形起伏度因子。采用粒子群优化(PSO)算法对支持向量机(SVM)模型的参数进行优化,得到最佳参数组合为惩罚因子c=1.42,核参数σ=1.15,将最优参数组合代入支持向量机模型中,构建出粒子群优化算法-支持向量机模型(PSO-SVM),并将其用于研究区滑坡易发性定量评价中。最后分别采用ROC曲线与Kappa系数对PSO-SVM模型性能的优越性进行检验,结果表明,PSO-SVM模型成功率与预测率分别为0.931和0.917,训练集与测试集的预测精度分别为79.17%、76.67%。研究结果可以为从事滑坡预测评价工作者提供决策参考。
Abstract
Landslide susceptibility assessment is a precondition of early warning and evaluation for regional landslide. Effective selection of hazard-inducing factors and establishment of assessment model are challenging in the prediction of landslide hazards. On the basis of the fusion of multi-source data including digital elevation model (DEM), geological map, road network map, and remote sensing image of Fugu County as a case study, the environmental factors such as landform and geomorphology, formation lithology and ground cover as well as inducing factors such as rainfall and human engineering activity were extracted as assessment indicators. The correlations among the extracted factors were analyzed and the topographic relief factor was eliminated. Furthermore, the particle swarm optimization (PSO) algorithm was adopted to optimize the parameters of support vector machine(SVM) model. The optimal parameters (penalty parameter c=1.42 and kernel parameter σ=1.15) were incorporated into the SVM model to establish the PSO-SVM model for landslide susceptibility assessment. The performance of the model was tested by the receiver operate curve (ROC) and Kappa coefficient, and results revealed that the success rate and the prediction rate of the PSO-SVM model were 0.931 and 0.917, respectively, and the prediction accuracy of train data and test data were 79.17% and 76.67%, respectively.
关键词
滑坡 /
评价指标 /
PSO-SVM模型 /
易发性评价 /
ROC曲线 /
Kappa系数
Key words
landslide /
assessment factors /
PSO-SVM model /
susceptibility assessment /
ROC /
Kappa coefficient
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 杨盼盼,王念秦,郭有金,等.基于加权信息量模型的临潼区滑坡易发性评价[J].长江科学院院报, 2019,36(12):1-9.
[2] 安凯强, 牛瑞卿. 信息量支持下SVM模型滑坡灾害易发性评价[J]. 长江科学院院报, 2016,33(8):47-51.
[3] 韩 玲, 张庭瑜, 张 恒. 基于IOE和SVM模型的府谷镇滑坡易发性分区[J]. 水土保持研究, 2019, 26(3):373-378.
[4] 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):4299-4312.
[5] 武雪玲, 沈少青, 牛瑞卿. GIS支持下应用PSO-SVM模型预测滑坡易发性[J]. 武汉大学学报(信息科学版), 2016,41(5):665-671.
[6] HE S W, PAN P, DAI L, et al. Application of Kernel-based Fisher Discriminant Analysis to Map Landslide Susceptibility in the Qinggan River Delta, Three Gorges, China[J].Geomorphology, 2012, 171/172: 30-41.
[7] NIETHAMMER U, JAMES M R, ROTHMUND S, et al. UAV-based Remote Sensing of the Super-Sauze Landslide: Evaluation and Results[J]. Engineering Geology, 2012,128: 2-11.
[8] MONDAL S, MANDAL S. Landslide Susceptibility Mapping of Darjeeling Himalaya, India Using Index of Entropy (IOE) Model[J]. Applied Geomatics, 2019, 11(2):129-146.
[9] 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.
[10] 齐 信,黄波林, 刘广宁, 等. 基于GIS技术和频率比模型的三峡地区秭归向斜盆地滑坡敏感性评价[J]. 地质力学学报, 2017, 23(1):97-104.
[11] 范 强, 巨能攀, 向喜琼, 等. 证据权法在区域滑坡危险性评价中的应用:以贵州省为例[J]. 工程地质学报, 2014, 22(3):474-481.
[12] 朱 莉, 卢毅敏, 罗建平. 基于灰色-Elman神经网络的区域滑坡易发性模型[J]. 自然灾害学报, 2013(5):122-128.
[13] 柯福阳, 李亚云. 基于BP神经网络的滑坡地质灾害预测方法[J]. 工程勘察, 2014(8):55-60.
[14] 向喜琼, 黄润秋. 基于GIS的人工神经网络模型在地质灾害危险性区划中的应用[J]. 中国地质灾害与防治学报, 2000, 11(3):23-27.
[15] 傅文杰. GIS支持下基于支持向量机的滑坡危险性评价[J]. 地理科学, 2008, 28(6):838-841.
[16] 许桂生. 滑坡灾害分析中的智能优化方法[J]. 水利工程建设, 2006(22):46-47.
[17] 胡 南.交叉验证法在GIS超声局部放电信号提取中的应用[J]. 山西电力,2017(4):6-9.
[18] 徐 苗. 基于人工鱼群算法的SVM参数优化[J]. 山西电子技术, 2019(1):30-33,61.
[19] 张晓南, 刘安心, 刘 斌,等. 基于优化PSO-SVM模型的软件可靠性预测[J]. 计算机应用, 2011(7):36-38,46.
基金
国家自然科学基金项目(41572287);陕西省科技统筹创新工程计划项目(2016KTCL03-19)
PDF(5346 KB)
