基于IOE和SVM模型的府谷镇滑坡易发性分区
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摘要
将陕西省府谷县府谷镇作为研究区,通过野外实地调查,圈定了47个滑坡点,制作了研究区滑坡编录图。以GIS软件和统计分析模型为基础,开展研究区滑坡易发性分区研究。首先通过GIS软件将滑坡点随机分成训练样本(70%)和测试样本(30%)两组。然后选择坡度、坡向、高程、距断层的距离、距道路的距离、距河流的距离、岩性、土地利用、NDVI、降雨量作为影响因子,提取因子图层。分别应用熵权模型(IOE)和支持向量机模型(SVM)计算滑坡易发性指数,利用自然间断点法将研究区划分为低易发区、中易发区、高易发区和极高易发区。最后利用ROC敏感度曲线下的面积(AUC)分别检验两种模型所得到的分区结果,结果表明,成功率曲线和预测度曲线的AUC值均在0.70～0.90,表明两种模型所得到的分区结果具有较高的精度,都可以为研究区的滑坡防治提供参考。在训练样本和测试样本中SVM模型的AUC值均最高,说明SVM模型比IOE模型适合在研究区开展滑坡预测研究。
Fugu Town, Fugu County, Shannxi Province, was taken as the reasearch area. Through field investigation, 47 landslides have been mapped in landslide inventory map. Based on GIS software and statistical analysis model, study of landslide susceptibility mapping was carried out. Then, slope aspect, slope angle, altitude, distance to fault, distance to road, distance to river, lithology, land use, NDVI and rainfall were selected as conditioning factors to extract the factor layer. The landslide susceptibility index was calculated using the index of entropy model(IOE) and the support vector machine model(SVM), respectively. The natural break method was used to divide the study area into low, moderate, high, and very high region. Finally, the area under the ROC sensitivity curve(AUC) was used to test the partition results obtained by these two models. The results show that the AUC values of success rate and prediction rate are between 0.70 and 0.90, indicating that the two landslide susceptibility maps have high accuracy and can provide reference for landslide control in study area. The AUC values of SVM model are the highest in the training and validating samples, which means that the SVM model is suitable for landslide prediction research in the study area than IOE model.
Fugu Town, Fugu County, Shannxi Province, was taken as the reasearch area. Through field investigation, 47 landslides have been mapped in landslide inventory map. Based on GIS software and statistical analysis model, study of landslide susceptibility mapping was carried out. Then, slope aspect, slope angle, altitude, distance to fault, distance to road, distance to river, lithology, land use, NDVI and rainfall were selected as conditioning factors to extract the factor layer. The landslide susceptibility index was calculated using the index of entropy model(IOE) and the support vector machine model(SVM), respectively. The natural break method was used to divide the study area into low, moderate, high, and very high region. Finally, the area under the ROC sensitivity curve(AUC) was used to test the partition results obtained by these two models. The results show that the AUC values of success rate and prediction rate are between 0.70 and 0.90, indicating that the two landslide susceptibility maps have high accuracy and can provide reference for landslide control in study area. The AUC values of SVM model are the highest in the training and validating samples, which means that the SVM model is suitable for landslide prediction research in the study area than IOE model.
引文
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[3]李郎平,兰恒星,郭长宝,等.基于改进频率比法的川藏铁路沿线及邻区地质灾害易发性分区评价[J].现代地质,2017,31(5):911-929.
[4]Pourghasemi,Hamid Reza,Mohammadi,et al.Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed,Iran[J].Arabian Journal of Geosciences,2013,6(7):2351-2365.
[5]阮沈勇,黄润秋.基于GIS的信息量法模型在地质灾害危险性区划中的应用[J].成都理工大学学报:自然科学版,2001,28(1):89-92.
[6]谭玉敏,郭栋,白冰心,等.基于信息量模型的涪陵区地质灾害易发性评价[J].地球信息科学学报,2015,17(12):1554-1562.
[7]王夏林,严宝文.基于熵权的可拓理论在地灾危险性评价中的应用[J].人民长江,2012,43(21):74-78.
[8]Chen W,Chai H,Sun X,et al.A GIS-based comparative study of frequency ratio,statistical index and weights-of-evidence models in landslide susceptibility mapping[J].Arabian Journal of Geosciences,2016,9(3):204-220.
[9]Mandal S,Mandal K.Modeling and mapping landslide susceptibility zones using GIS based multivariate binary logistic regression(LR)model in the Rorachu river basin of eastern Sikkim Himalaya,India[J].Modeling Earth Systems&Environment,2018,4(1):69-88.
[10]Yilmaz I.A case study from Koyulhisar(Sivas-Turkey)for landslide susceptibility mapping by artificial neural networks[J].Bulletin of Engineering Geology&the Environment,2009,68(3):297-306.
[11]姜琪文,许强,何政伟.基于SVM多类分类的滑坡区域危险性评价方法研究[J].地质灾害与环境保护,2005,16(3):328-330.
[12]Yao X,Tham L G,Dai F C.Landslide susceptibility mapping based on Support Vector Machine:A case study on natural slopes of Hong Kong,China[J].Geomorphology,2008,101(4):0-582.
[13]Pradhan B,Pourghasemi H R,Jirandeh A G,et al.Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province,Iran[J].Journal of Earth System Science,2013,122(2):349-369.
[14]陕西省地质矿产局.陕西省区域地质志[M].北京:地质出版社,1989.
[15]尹星星.基于熵值法新疆循环经济发展综合评价分析[J].再生资源与循环经济,2014,7(7):15-18.
[16]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.
[17]许冲,徐锡伟.基于不同核函数的2010年玉树地震滑坡空间预测模型研究[J].地球物理学报,2012,55(9):2994-3005.
[18]Dai F C,Lee C F,Ngai Y Y.Landslide risk assessment and management:An overview[J].Engineering Geology,2002,64(1):65-87.
[19]李明,王伟,张超.基于ArcGIS信息量模型的神农溪流域地质灾害易发性区划[J].安全与环境工程,2013,20(2):46-52.
[20]Bai S B,Jian W,Zhou P G,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.