顾及样本优化选择的多核支持向量机滑坡灾害易发性分析评价

doi:10.11947/j.AGCS.2022.20220326

摘要/Abstract

摘要： 滑坡灾害易发性分析评价对地质灾害的防治与管理具有重要意义。针对滑坡灾害样本选择策略,单核支持向量机多特征映射不合理的问题,本文提出顾及样本优化选择的多核支持向量机(multiple kernel support vector machine,MKSVM)滑坡灾害易发性分析评价方法。为了保证样本平衡性并提高负样本的合理性,采用相对频率比(relative frequency,RF)综合评价各状态对于滑坡灾害易发性影响的重要程度,实现各评价因子状态的合理划分;利用确定性系数法(certainty factor,CF)计算各评价因子各状态分级影响滑坡灾害的敏感性,并在此基础上进行加权求和得到各栅格单元的滑坡灾害易发性指数,在滑坡灾害易发性指数极低和低易发区内随机选择与滑坡灾害点数目一致的非滑坡灾害点作为负样本数据。利用MKSVM对各特征空间最优核函数进行线性组合,解决了单一核函数映射不合理的问题,提高了模型的分类准确率和预测精度。以湖南省湘西土家族苗族自治州为研究区,从滑坡灾害易发性分区图、分区统计及评价模型精度3个方面对CF样本策略的MKSVM模型、CF样本策略的单核SVM模型、随机样本策略的MKSVM模型、随机样本策略的单核SVM模型进行了对比分析。结果表明,4种模型的受试者工作特征曲线(receiver operating characteristic,ROC)下的面积(area under curve,AUC)分别为0.859、0.809、0.798、0.766,验证了CF样本策略的合理性、有效性及MKSVM模型的可靠性。

关键词: 滑坡, 易发性, 确定性系数, 多核, 支持向量机

Abstract: The analysis and evaluation of landslide disaster susceptibility is of great significance to the prevention and management of geological disasters. In view of the sample selection strategy and the unreasonable multi-feature mapping in single-kernel vector machine, this paper proposes the landslide susceptibility analysis and evaluation method of multiple kernel support vector machine (MKSVM) considering the sample optimization selection. To ensure sample balance and improve the plausibility of negative samples, using the relative frequency ratio (relative frequency, RF) comprehensively evaluate the importance degree of each state in the influence of landslide disaster susceptibility, the purpose is to realize the reasonable division of each evaluation factor state; Using the deterministic coefficient method (certainty factor, CF) calculates the sensitivity of each state of each evaluation factor, the weighted sum has obtained the landslide disaster susceptibility index of each grid cell, non-landslide disaster points consistent with the number of landslide disaster points were randomly selected in the very low and low landslide disaster prone index as the negative sample data. Then, multi-kernel learning is used to select the SVM optimal kernel function and to linear combine the optimal kernel functions in each feature space to avoid unreasonable mapping of a single kernel function, and it improve the classification accuracy and prediction accuracy of the model. Taking Xiangxi Tujia and Miao Autonomous Prefecture of Hunan province as the research area, MKSVM model of CF sample strategy, single-kernel SVM model of CF sample strategy, MKSVM model of random sample strategy and single-kernel SVM model of random sample strategy were compared analyzed from three aspects of landslide disaster prone zoning map, partition statistics and evaluation model accuracy. The results indicate that the subject operating characteristic curves of the four models (receiver operating characteristic, area under the ROC) (area under curve, AUC) were 0.859,0.809,0.798,0.766, the rationality and validity of the CF sample strategy and the reliability of the MKSVM model are verified.

Key words: landslide, susceptibility, certainty factor, multi-kernel, support vector machine

中图分类号:

P227

刘纪平, 梁恩婕, 徐胜华, 刘猛猛, 王勇, 张福浩, 罗安. 顾及样本优化选择的多核支持向量机滑坡灾害易发性分析评价[J]. 测绘学报, 2022, 51(10): 2034-2045.

LIU Jiping, LIANG Enjie, XU Shenghua, LIU Mengmeng, WANG Yong, ZHANG Fuhao, LUO An. Multi-kernel support vector machine considering sample optimization selection for analysis and evaluation of landslide disaster susceptibility[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10): 2034-2045.

参考文献

[1] 朱建军,李志伟,胡俊.InSAR变形监测方法与研究进展[J]. 测绘学报,2017,46(10):1717-1733. DOI: 10.11947/j.AGCS.2017.20170350. ZHU Jianjun, LI Zhiwei, HU Jun. Research progress and methods of InSAR for deformation monitoring[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1717-1733. DOI: 10.11947/j.AGCS.2017.20170350.
[2] 赵超英, 刘晓杰, 张勤, 等. 甘肃黑方台黄土滑坡InSAR识别、监测与失稳模式研究[J]. 武汉大学学报(信息科学版), 2019, 44(7): 996-1007. ZHAO Chaoying, LIU Xiaojie, ZHANG Qin, et al. Research on loess landslide identification, monitoring and failure mode with InSAR technique in Heifangtai, Gansu[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 996-1007.
[3] 白正伟, 张勤, 黄观文, 等. “轻终端+行业云”的实时北斗滑坡监测技术[J]. 测绘学报, 2019, 48(11): 1424-1429. DOI: 10.11947/j.AGCS.2019.20190167. BAI Zhengwei, ZHANG Qin, HUANG Guanwen, et al. Real-time BeiDou landslide monitoring technology of “light terminal plus industry cloud”[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(11): 1424-1429. DOI: 10.11947/j.AGCS.2019.20190167.
[4] 李振洪, 宋闯, 余琛, 等. 卫星雷达遥感在滑坡灾害探测和监测中的应用: 挑战与对策[J]. 武汉大学学报(信息科学版), 2019, 44(7): 967-979. LI Zhenhong, SONG Chuang, YU Chen, et al. Application of satellite radar remote sensing to landslide detection and monitoring: challenges and solutions[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 967-979.
[5] LIU Chun, LI Weiyue, WU Hangbin, et al. Susceptibility evaluation and mapping of China's landslides based on multi-source data[J]. Natural Hazards, 2013, 69(3): 1477-1495.
[6] 刘璐瑶,高惠瑛.基于证据权与Logistic回归模型耦合的滑坡易发性评价[J/OL].工程地质学报:1-11[2022-10-26].DOI:10.13544/j.cnki.jeg.2020-482. LIU Luyao, GAO Huiying. Evaluation of the landslide susceptibility based on the coupling of the evidence weights and the Logistic regression model [J/OL]. Engineering Geological Journal: 1-11.[2022-10-26].DOI:10.13544/j.cnki.jeg.2020-482.
[7] 胡燕, 李德营, 孟颂颂, 等. 基于证据权法的巴东县城滑坡灾害易发性评价[J]. 地质科技通报, 2020, 39(3): 187-194. HU Yan, LI Deying, MENG Songsong, et al. Landslide susceptibility evaluation in Badong county based on weights of evidence method[J]. Bulletin of Geological Science and Technology, 2020, 39(3): 187-194.
[8] 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 of Rock Mechanics and Engineering, 2014, 33(4): 797-808.
[9] 李远远, 梅红波, 任晓杰, 等. 基于确定性系数和支持向量机的地质灾害易发性评价[J]. 地球信息科学学报, 2018, 20(12): 1699-1709. LI Yuanyuan, MEI Hongbo, REN Xiaojie, et al. Geological disaster susceptibility evaluation based on certainty factor and support vector machine[J]. Journal of Geo-Information Science, 2018, 20(12): 1699-1709.
[10] LONG N, DE SMEDT F. Analysis and mapping of rainfall-induced landslide susceptibility in A Luoi district, Thua Thien Hue province, Vietnam[J]. Water, 2018, 11(1): 51.
[11] WU Yanli, LI Wenping, WANG Qiqing, et al. Landslide susceptibility assessment using frequency ratio, statistical index and certainty factor models for the Gangu county, China[J]. Arabian Journal of Geosciences, 2016, 9(2): 1-16.
[12] 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.
[13] 田乃满, 兰恒星, 伍宇明, 等. 人工神经网络和决策树模型在滑坡易发性分析中的性能对比[J]. 地球信息科学学报, 2020, 22(12): 2304-2316. TIAN Naiman, LAN Hengxing, WU Yuming, et al. Performance comparison of BP artificial neural network and CART decision tree model in landslide susceptibility prediction[J]. Journal of Geo-Information Science, 2020, 22(12): 2304-2316.
[14] 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): 1085-1091. 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): 1085-1091.
[15] 毛伊敏, 周昭飞, 彭喆, 等. 基于不确定多分类支持向量机在滑坡危险性预测的应用[J]. 江西理工大学学报, 2016, 37(3): 102-108. MAO Yimin, ZHOU Zhaofei, PENG Zhe, et al. Landslide hazard prediction based on uncertain multi-classification support vector machine method[J]. Journal of Jiangxi University of Science and Technology, 2016, 37(3): 102-108.
[16] BENDAHMANE A, BENYETTOU A. Learning to generate optimized term weighting for web documents classification: a parallel mimetic approach based on support vector machines[J]. International Review on Computers and Software (IRECOS), 2016, 11(12): 1147.
[17] GACHOKI P, MBURU M, MURAYA M. Predictive modelling of benign and malignant tumors using binary logistic, support vector machine and extreme gradient boosting models [J]. American Journal of Applied Mathematics and Statistics, 2019, 7(6): 196-204.
[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] HU Qiao, ZHOU Yi, WANG Shixing, et al. Machine learning and fractal theory models for landslide susceptibility mapping: case study from the Jinsha River basin[J]. Geomorphology, 2020, 351: 106975.
[20] 徐胜华, 刘纪平, 王想红, 等. 熵指数融入支持向量机的滑坡灾害易发性评价方法——以陕西省为例[J]. 武汉大学学报(信息科学版), 2020, 45(8): 1214-1222. XU Shenghua, LIU Jiping, WANG Xianghong, et al. Landslide susceptibility assessment method incorporating index of entropy based on support vector machine: a case study of Shaanxi province[J]. Geomatics and Information Science of Wuhan University, 2020, 45(8): 1214-1222.
[21] 林荣福, 刘纪平, 徐胜华, 等. 随机森林赋权信息量的滑坡易发性评价方法[J]. 测绘科学, 2020, 45(12): 131-138. LIN Rongfu, LIU Jiping, XU Shenghua, et al. Evaluation method of landslide susceptibility based on random forest weighted information[J]. Science of Surveying and Mapping, 2020, 45(12): 131-138.
[22] LIU Mengmeng, LIU Jiping, XU Shenghua, et al. Landslide susceptibility mapping with the fusion of multi-feature SVM model based FCM sampling strategy: a case study from Shaanxi province[J]. International Journal of Image and Data Fusion, 2021, 12(4): 349-366.
[23] FANG Zhice, WANG Yi, DUAN Hexiang, et al. Comparison of general kernel, multiple kernel, infinite ensemble and semi-supervised support vector machines for landslide susceptibility prediction[J]. Stochastic Environmental Research and Risk Assessment, 2022, 36(3): 1-22.
[24] 易波琳. 大湘西地区滑坡地质灾害影响因素及形成机制分析[J]. 地球, 2016(11): 390-391. Yi Bolin. Analysis of influencing factors and formation mechanism of landslide geological disasters in Great Xiangxi region [J]. The Earth, 2016(11): 390-391.
[25] 张福浩, 朱月月, 赵习枝, 等. 地理因子支持下的滑坡隐患点空间分布特征及识别研究[J]. 武汉大学学报(信息科学版), 2020, 45(8): 1233-1244. ZHANG Fuhao, ZHU Yueyue, ZHAO Xizhi, et al. Spatial distribution and identification of hidden danger points of landslides based on geographical factors[J]. Geomatics and Information Science of Wuhan University, 2020, 45(8): 1233-1244.
[26] 刘璐瑶, 高惠瑛, 李照. 基于CF与Logistic回归模型耦合的永嘉县滑坡易发性评价[J]. 中国海洋大学学报(自然科学版), 2021, 51(10): 121-129. LIU Luyao, GAO Huiying, LI Zhao. Landslide susceptibility assessment based on coupling of CF model and logistic regression model in Yongjia county[J]. Periodical of Ocean University of China, 2021, 51(10): 121-129.
[27] LEE Saro, HONG Soomin, JUNG Hyungsup. A support vector machine for landslide susceptibility mapping in Gangwon province, Korea[J]. Sustainability, 2017, 9(1): 48.
[28] 孙德亮. 基于机器学习的滑坡易发性区划与降雨诱发滑坡预报预警研究[D]. 上海: 华东师范大学, 2019. SUN Deliang. Mapping landslide susceptibility based on machine learning and forecast warning of landslide induced by rainfall[D]. Shanghai: East China Normal University, 2019.
[29] XU Shenghua, ZHANG Meng, MA Yu, et al. Multiclassification method of landslide risk assessment in consideration of disaster levels: a case study of Xianyang city, Shaanxi province[J]. ISPRS International Journal of Geo-Information, 2021, 10(10): 646.
[30] 刘月, 王宁涛, 周超, 等. 基于ROC曲线与确定性系数法集成模型的三峡库区奉节县滑坡易发性评价[J]. 安全与环境工程, 2020, 27(4): 61-70. LIU Yue, WANG Ningtao, ZHOU Chao, et al. Evaluation of landslide susceptibility based on ROC and certainty factor method in Fengjie county, Three Gorges Reservoir[J]. Safety and Environmental Engineering, 2020, 27(4): 61-70.
[31] CHEN Zhuo, LIANG Shouyun, KE Yutian, et al. Landslide susceptibility assessment using evidential belief function, certainty factor and frequency ratio model at Baxie River basin, NW China[J]. Geocarto International, 2019, 34(4): 348-367.
[32] FAN Wen, WEI Xinsheng, CAO Yanbo, et al. Landslide susceptibility assessment using the certainty factor and analytic hierarchy process[J]. Journal of Mountain Science, 2017, 14(5): 906-925.
[33] MAHAJAN S, RAINA A, GAO Xiaozhi, et al. Plant recognition using morphological feature extraction and transfer learning over SVM and AdaBoost[J]. Symmetry, 2021, 13(2): 356.
[34] 陈军. 全极化SAR分类若干关键技术研究[D]. 徐州: 中国矿业大学, 2015. CHEN Jun. Research on some key techniques for fully polarimetric SAR image classification[D]. Xuzhou: China University of Mining and Technology, 2015.
[35] 张宪法, 郝矿荣, 陈磊. 免疫多域特征融合的多核学习SVM运动想象脑电信号分类[J]. 自动化学报, 2020, 46(11): 2417-2426. ZHANG Xianfa, HAO Kuangrong, CHEN Lei. Motor imagery EEG classification based on immune multi-domain-feature fusion and multiple kernel learning SVM[J]. Acta Automatica Sinica, 2020, 46(11): 2417-2426.
[36] 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. 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): 4299-4312.
[37] 张晓敏. 基于GIS的陕西省滑坡灾害危险性评价及分区研究[D]. 西安: 长安大学, 2019. ZHANG Xiaomin. Hazard assessment and zoning research of landslide in Shaanxi province based on GIS[D]. Xi'an: Changan University, 2019.
[38] 闫举生, 谭建民. 基于不同因子分级法的滑坡易发性评价——以湖北远安县为例[J]. 中国地质灾害与防治学报, 2019, 30(1): 52-60. YAN Jusheng, TAN Jianmin. Landslide susceptibility assessment based on different factor classification methods: a case study in Yuan'an county of Hubei province[J]. The Chinese Journal of Geological Hazard and Control, 2019, 30(1): 52-60.
[39] 武雪玲, 任福, 牛瑞卿. 多源数据支持下的三峡库区滑坡灾害空间智能预测[J]. 武汉大学学报(信息科学版), 2013, 38(8): 963-968. WU Xueling, REN Fu, NIU Ruiqing. Spatial intelligent prediction of landslide hazard based on multi-source data in Three Gorges Reservoir area[J]. Geomatics and Information Science of Wuhan University, 2013, 38(8): 963-968.
[40] 许强, 朱星, 李为乐, 等. “天-空-地”协同滑坡监测技术进展[J]. 测绘学报,2022,51(7):1416-1436. DOI: 10.11947/j.AGCS.2022.20220320. XU Qiang, ZHU Xing, LI Weile, et al. Technical progress of space-air-ground collaborative monitoring of landslide[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7): 1416-1436. DOI: 10.11947/j.AGCS.2022.20220320.
[41] 张勤, 赵超英, 陈雪蓉. 多源遥感地质灾害早期识别技术进展与发展趋势[J]. 测绘学报,2022,51(6):885-896. DOI: 10.11947/j.AGCS.2022.20220132. ZHANG Qin, ZHAO Chaoying, CHEN Xuerong. Technical progress and development trend of geological hazards early identification with multi-source remote sensing[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(6): 885-896. DOI: 10.11947/j.AGCS.2022.20220132.