不确定GM-CFSFDP聚类算法在滑坡危险性预测中的应用
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摘要
针对滑坡危险性预测中降雨等不确定诱发因素难以有效处理,CFSFDP算法需要人工尝试设置密度阈值以及对大规模数据集无法进行准确聚类等问题,为了提高滑坡危险性预测准确度,提出一种基于网格与类合并的不确定CFSFDP(简称不确定GM-CFSFDP)聚类算法.该算法首先引入不确定数据处理方法,设计了E-ML距离公式,有效刻画降雨不确定因素;其次通过网格划分的思想把大规模数据集划分到多个网格空间中,实现大规模数据有效编码;计算网格平均密度,建立网格密度阈值分布模型,动态获得网格密度阈值;最后利用层次聚类思想对关联性较高的类进行合并,构建不确定GM-CFSFDP算法模型,在延安宝塔区进行滑坡实例验证.实验结果表明不确定GMCFSFDP聚类算法获得较高的预测精度,从而验证了该算法在滑坡危险性预测中的可行性和先进性.
Since the rainfall and other uncertainties are difficult to effectively deal with in landside hazard prediction, as well as the density threshold in CFSFDP algorithm is required to be set manually and its low accuracy for large-scale data clustering, in order to improve the prediction accuracy, this study proposed an uncertain CFSFDP algorithm based on Grid and Merging clusters(uncertain GM-CFSFDP). Firstly, the E-ML distance formula based on uncertain data processing method is designed to effectively describe the uncertain factors of rainfall. Secondly, the idea of meshing is used to effectively encode the large-scale data by dividing it into multiple grid spaces. The average density of the mesh is calculated to establish the grid density threshold distribution model and obtain the grid density threshold dynamically.Finally, the hierarchical clustering idea is used to merge the higher association class and the uncertain GM-CFSFDP algorithm model is established. The experiments conducted in the Baota district of Yan'an show that the uncertain GMCFSFDP clustering algorithm achieves a higher prediction accuracy and proves the feasibility and advancement of the algorithm in landslide hazard prediction.
Since the rainfall and other uncertainties are difficult to effectively deal with in landside hazard prediction, as well as the density threshold in CFSFDP algorithm is required to be set manually and its low accuracy for large-scale data clustering, in order to improve the prediction accuracy, this study proposed an uncertain CFSFDP algorithm based on Grid and Merging clusters(uncertain GM-CFSFDP). Firstly, the E-ML distance formula based on uncertain data processing method is designed to effectively describe the uncertain factors of rainfall. Secondly, the idea of meshing is used to effectively encode the large-scale data by dividing it into multiple grid spaces. The average density of the mesh is calculated to establish the grid density threshold distribution model and obtain the grid density threshold dynamically.Finally, the hierarchical clustering idea is used to merge the higher association class and the uncertain GM-CFSFDP algorithm model is established. The experiments conducted in the Baota district of Yan'an show that the uncertain GMCFSFDP clustering algorithm achieves a higher prediction accuracy and proves the feasibility and advancement of the algorithm in landslide hazard prediction.
引文
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4 文建华,周翠英,黄林冲,等.边坡稳定性分类评价的同伦模糊C-均值聚类算法.岩土力学,2012,33(5):1457–1461.
5孙树林,余文平,刘小芳,等.基于信息熵与KPSO聚类法滑坡敏感性分析.环境保护科学,2014,40(6):88–96.
6 吴亚子,杨敏.灰色聚类法在阿里地区地质灾害危险性评价中的应用.水资源与水工程学报,2010,21(6):155–158.
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12王飞,王国胤,李智星,等.一种基于网格的密度峰值聚类算法.小型微型计算机系统,2017,38(5):1034–1038.
13 邢长征,王晓旭.基于扩展网格和密度的数据流聚类算法.计算机工程,2014,40(12):188–194.[doi:10.3778/j.issn.1002-8331.1207-0101]
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15 蒋礼青,张明新,郑金龙,等.快速搜索与发现密度峰值聚类算法的优化研究.计算机应用研究,2016,33(11):3251–3254.
16李宗林,罗可.DBSCAN算法中参数的自适应确定.计算机工程与应用,2016,52(3):70–73.
17 孙昊,张明新,戴娇,等.基于网格的快速搜寻密度峰值的聚类算法优化研究.计算机工程与科学,2017,39(5):964 –970.
18乔端瑞.基于K-means算法及层次聚类算法的研究与应用[硕士学位论文].长春:吉林大学,2016.
19 吕琳,尉永清,任敏,等.基于蚁群优化算法的凝聚型层次聚类.计算机应用研究,2017,34(1):114–117.
20刘卫明,高晓东,毛伊敏,等.不确定遗传神经网络在滑坡危险性预测中的研究与应用.计算机工程,2017,43(2):308 –316.
21 Yeon YK,Han JG,Ryu KH.Landslide susceptibility mapping in Injae,Korea,using a decision tree.Engineering Geology,2010,116(3-4):274–283.[doi:10.1016/j.enggeo.2010.09.009]
22王磊,张春山,杨为民,等.基于GIS的甘肃省甘谷县地质灾害危险性评价.地质力学学报,2011,17(4):388–401.
23 邱海军.区域滑坡崩塌地质灾害特征分析及其易发性和危险性评价研究[博士学位论文].西安:西北大学,2012.
24辛鹏,吴树仁,石菊松,等.基于降雨响应的黄土丘陵区滑坡危险性预测研究——以宝鸡市麟游县为例.地球学报,2012,33(3):349–359.
2 Salciarini D,Fanelli G,Tamagnini C.A probabilistic model for rainfall—induced shallow landslide prediction at the regional scale.Landslides,2017,14(5):1731–1746.[doi:10 .1007/s10346-017-0812-0]
3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.岩石力学与工程学报,2016,35(2):284–296.
4 文建华,周翠英,黄林冲,等.边坡稳定性分类评价的同伦模糊C-均值聚类算法.岩土力学,2012,33(5):1457–1461.
5孙树林,余文平,刘小芳,等.基于信息熵与KPSO聚类法滑坡敏感性分析.环境保护科学,2014,40(6):88–96.
6 吴亚子,杨敏.灰色聚类法在阿里地区地质灾害危险性评价中的应用.水资源与水工程学报,2010,21(6):155–158.
7Yang MS,Nataliani Y.Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters.Pattern Recognition,2017,71:45–59.[doi:10.1016/j.patcog.2017.05.017]
8赵文冲,蔡江辉,张继福.改进k值自动获取VDBSCAN聚类算法.计算机系统应用,2016,25(9):131–136.[doi:10 .15888/j.cnki.csa.005325]
9 Rodriguez A,Laio A.Clustering by fast search and find of density peaks.Science,2014,344(6191):1492–1496.[doi:10 .1126/science.1242072]
10Tran L,Duckstein L.Comparison of fuzzy numbers using a fuzzy distance measure.Fuzzy Sets and Systems,2002,130 (3):331–341.[doi:10.1016/S0165-0114(01)00195-6]
11 刘华文.基于距离测度的模糊数排序.山东大学学报(理学版),2004,39(2):30–36.
12王飞,王国胤,李智星,等.一种基于网格的密度峰值聚类算法.小型微型计算机系统,2017,38(5):1034–1038.
13 邢长征,王晓旭.基于扩展网格和密度的数据流聚类算法.计算机工程,2014,40(12):188–194.[doi:10.3778/j.issn.1002-8331.1207-0101]
14米源,杨燕,李天瑞.基于密度网格的数据流聚类算法.计算机科学,2011,38(12):178–181.[doi:10.3969/j.issn.1002-137 X.2011.12.040]
15 蒋礼青,张明新,郑金龙,等.快速搜索与发现密度峰值聚类算法的优化研究.计算机应用研究,2016,33(11):3251–3254.
16李宗林,罗可.DBSCAN算法中参数的自适应确定.计算机工程与应用,2016,52(3):70–73.
17 孙昊,张明新,戴娇,等.基于网格的快速搜寻密度峰值的聚类算法优化研究.计算机工程与科学,2017,39(5):964 –970.
18乔端瑞.基于K-means算法及层次聚类算法的研究与应用[硕士学位论文].长春:吉林大学,2016.
19 吕琳,尉永清,任敏,等.基于蚁群优化算法的凝聚型层次聚类.计算机应用研究,2017,34(1):114–117.
20刘卫明,高晓东,毛伊敏,等.不确定遗传神经网络在滑坡危险性预测中的研究与应用.计算机工程,2017,43(2):308 –316.
21 Yeon YK,Han JG,Ryu KH.Landslide susceptibility mapping in Injae,Korea,using a decision tree.Engineering Geology,2010,116(3-4):274–283.[doi:10.1016/j.enggeo.2010.09.009]
22王磊,张春山,杨为民,等.基于GIS的甘肃省甘谷县地质灾害危险性评价.地质力学学报,2011,17(4):388–401.
23 邱海军.区域滑坡崩塌地质灾害特征分析及其易发性和危险性评价研究[博士学位论文].西安:西北大学,2012.
24辛鹏,吴树仁,石菊松,等.基于降雨响应的黄土丘陵区滑坡危险性预测研究——以宝鸡市麟游县为例.地球学报,2012,33(3):349–359.
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