基于粒子群优化支持向量机的延长县滑坡易发性评价
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摘要
参数优化问题直接影响着支持向量机的预测精度和泛化能力,粒子群优化算法具有全局最优搜索能力,因此通过粒子群算法优化支持向量机参数可以有效提高预测精度。以延长县历史滑坡数据为基础,分析了岩性、地貌类型、土壤厚度、坡度、坡向、坡高与滑坡分布的关系,并利用滑坡密度值对各定性或定量因子进行了归一化处理;在此基础上,通过区域内所划分的16 300个斜坡单元作为评价单元,采用粒子群优化支持向量机(PSO-SVM)算法完成了延长县滑坡的易发性评价。从滑坡密度指标角度来看,评价结果中高易发区和极高易发区的历史滑坡数占比72.19%,通过滑坡面积百分比(LAR)等指标进行了有效的验证,均显示出对滑坡易发性评价效果良好。
The parameter optimization directly affects the prediction accuracy and generalization of SVM. The particle swarm optimization algorithm has global optimal search ability. Therefore, optimizing the support vector machine parameters by particle swarm optimization can effectively improve the prediction accuracy. Based on the historical landslide data of Yanchang County, this paper explores and analyzes the hazard factors of landslides, and uses ArcGIS platform to extract and analyze the relationship between lithology, landform, loess thickness, slope, slope direction, slope height and landslide distribution, and to use landslide density to normalize each qualitative or quantitative factor. On the basis of this, the PSO-SVM algorithm was used to evaluate the susceptibility of Yanchang County landslide through the area 16 300 divided by the slope unit as the evaluation unit. From the perspective of landslide density index, the number of historical landslides in the high-prone and ultra-high-prone areas account for 72.19%, validated by landslide percentage(LAR) and other indicators.Both show good susceptibility evaluation results.
The parameter optimization directly affects the prediction accuracy and generalization of SVM. The particle swarm optimization algorithm has global optimal search ability. Therefore, optimizing the support vector machine parameters by particle swarm optimization can effectively improve the prediction accuracy. Based on the historical landslide data of Yanchang County, this paper explores and analyzes the hazard factors of landslides, and uses ArcGIS platform to extract and analyze the relationship between lithology, landform, loess thickness, slope, slope direction, slope height and landslide distribution, and to use landslide density to normalize each qualitative or quantitative factor. On the basis of this, the PSO-SVM algorithm was used to evaluate the susceptibility of Yanchang County landslide through the area 16 300 divided by the slope unit as the evaluation unit. From the perspective of landslide density index, the number of historical landslides in the high-prone and ultra-high-prone areas account for 72.19%, validated by landslide percentage(LAR) and other indicators.Both show good susceptibility evaluation results.
引文
[1] 许冲,徐锡伟.基于GIS与ANN模型的地震滑坡易发性区划[J].地质科技情报,2012,31(3):116-121.
[2] 王芳,殷坤龙,桂蕾,等.不同日降雨工况下万州区滑坡灾害危险性分析[J].地质科技情报,2018,37(1):190-195,203.
[3] 于宪煜,胡友健,牛瑞卿.基于RS-SVM模型的滑坡易发性评价因子选择方法研究[J].地理与地理信息科学,2016,32(3):23-28.
[4] Dai F C,Lee C F,Ngai Y Y.Landslide risk assessment and management:An overview[J].Engineering Geology,2002,64(1):65-87.
[5] 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(增刊2):3978-3985.
[6] Xu C,Xu X,Dai F,et al.Application of an incomplete landslide inventory,logistic regression model and its validation for landslide susceptibility mapping related to the May 12,2008 Wenchuan earthquake of China[J].Natural Hazards,2013,68(2):883-900.
[7] 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,2013,22(5):120-126.
[8] 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,2014,42(8):55-60.
[9] Bai S B,Wang J,Lü G N,et al.GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area,China[J].Geomorphology,2010,115(1):23-31.
[10] Youssef A M,Pradhan B,Pourghasemi H R,et al.Landslide susceptibility assessment at Wadi Jawrah Basin,Jizan region,Saudi Arabia using two bivariate models in GIS[J].Geosciences Journal,2015,19(3):449-469.
[11] Fell R,Corominas J,Bonnard C,et al.Guidelines for landslide susceptibility,hazard and risk zoning for land use planning[J].Engineering Geology,2008,102(3/4):85-98.
[12] 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.
[13] 胡瑞林,范林峰,王珊珊,等.滑坡风险评价的理论与方法研究[J].工程地质学报,2013,21(1):76-84.
[14] 武雪玲,任福,牛瑞卿,等.斜坡单元支持下的滑坡易发性评价支持向量机模型[J].武汉大学学报:信息科学版,2013,38(12):1499-1503.
[15] 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.
[16] Ardjani F,Sadouni K,Benyettou M.Optimization of SVM MultiClass by Particle Swarm(PSO-SVM)[J].International Journal of Modern Education & Computer Science,2010,2(2):1-4.
[17] Izmailov R,Vapnik V,Vashist A.Multidimensional splines with infinite number of knots as SVM kernels[C]//Anon.International Joint Conference on Neural Networks.2013.
[18] 权文,王晓丹,王坚,等.基于SVM概率输出与证据理论的多分类方法[J].计算机工程,2012,38(5):167-169.
[2] 王芳,殷坤龙,桂蕾,等.不同日降雨工况下万州区滑坡灾害危险性分析[J].地质科技情报,2018,37(1):190-195,203.
[3] 于宪煜,胡友健,牛瑞卿.基于RS-SVM模型的滑坡易发性评价因子选择方法研究[J].地理与地理信息科学,2016,32(3):23-28.
[4] Dai F C,Lee C F,Ngai Y Y.Landslide risk assessment and management:An overview[J].Engineering Geology,2002,64(1):65-87.
[5] 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(增刊2):3978-3985.
[6] Xu C,Xu X,Dai F,et al.Application of an incomplete landslide inventory,logistic regression model and its validation for landslide susceptibility mapping related to the May 12,2008 Wenchuan earthquake of China[J].Natural Hazards,2013,68(2):883-900.
[7] 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,2013,22(5):120-126.
[8] 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,2014,42(8):55-60.
[9] Bai S B,Wang J,Lü G N,et al.GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area,China[J].Geomorphology,2010,115(1):23-31.
[10] Youssef A M,Pradhan B,Pourghasemi H R,et al.Landslide susceptibility assessment at Wadi Jawrah Basin,Jizan region,Saudi Arabia using two bivariate models in GIS[J].Geosciences Journal,2015,19(3):449-469.
[11] Fell R,Corominas J,Bonnard C,et al.Guidelines for landslide susceptibility,hazard and risk zoning for land use planning[J].Engineering Geology,2008,102(3/4):85-98.
[12] 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.
[13] 胡瑞林,范林峰,王珊珊,等.滑坡风险评价的理论与方法研究[J].工程地质学报,2013,21(1):76-84.
[14] 武雪玲,任福,牛瑞卿,等.斜坡单元支持下的滑坡易发性评价支持向量机模型[J].武汉大学学报:信息科学版,2013,38(12):1499-1503.
[15] 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.
[16] Ardjani F,Sadouni K,Benyettou M.Optimization of SVM MultiClass by Particle Swarm(PSO-SVM)[J].International Journal of Modern Education & Computer Science,2010,2(2):1-4.
[17] Izmailov R,Vapnik V,Vashist A.Multidimensional splines with infinite number of knots as SVM kernels[C]//Anon.International Joint Conference on Neural Networks.2013.
[18] 权文,王晓丹,王坚,等.基于SVM概率输出与证据理论的多分类方法[J].计算机工程,2012,38(5):167-169.
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