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Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management 总被引:1,自引:0,他引:1
Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70%landslide pixels)and validation data(30%landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20%interval of landslide occurrence probability contained 89%of the historical landslides but only accounted for 10.3%of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices. 相似文献
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采用微生物宏基因组学微阵列GeoChip 5.0技术,选择腾格里沙漠东南缘沙坡头地区不同年代人工固沙植被区的生物土壤结皮(BSC)为对象,分析BSC演替过程中参与铁代谢的功能微生物组成及其功能基因变化特征,研究微生物铁代谢对BSC演替的响应及调控。结果表明:真菌参与铁吸收和转运过程,古菌参与铁转运和贮存过程,细菌则在铁代谢吸收、转运和贮存过程中均起主要调控作用。门水平上,BSC铁代谢功能微生物组成变化对演替的响应不敏感,BSC铁代谢微生物主要为变形菌门(Proteobacteria)。BSC铁代谢功能基因多样性的显著提高和三类铁代谢过程基因信号强度达到最高水平需要经过61 a的演替。调控BSC铁吸收过程的主要功能基因为亚铁氧化酶编码基因iro;调控原核生物铁转运过程的主要功能基因,为羟基苯甲酰丝氨酸铁外膜转运体编码基因cirA和Fe(Ⅱ)转运蛋白编码基因feoB,真菌铁转运过程主要依靠含铁细胞转运体和铁氧化酶高亲和力的作用;调控铁贮存过程的主要功能基因为固定相类核蛋白编码基因dps。在BSC演替阶段末期,上述铁代谢功能基因强度的显著增加促进了微生物的铁代谢潜能。干旱、半干旱荒漠生态系统植被恢复过程中微生物铁代谢潜能的恢复需要较长时间。 相似文献
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我国的胃癌发病率高,每年新增胃癌患者占全世界每年新增数量的42%,胃癌成为我国恶性肿瘤防控的重点.本文针对胃癌数据的特征,给出数据预处理和集成方法;采用C5.0分类算法,构建了胃癌生存预测模型,并首次采用美国癌症研究所的SEER数据库进行预测实验.实验结果表明:C5.0预测的精确度、特异性均高于BP-神经网络算法;胃癌患者的出生地点与最终的存活状态之间存在较强的相关性.该研究是数据挖掘技术在医学领域的一个实际应用,对胃癌的临床诊断具有一定的参考价值,可为医生制定合理的治疗和预防方案提供一定参考. 相似文献
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MA Haiping WANG Qian ZHANG Bo WU Shanyi WANG Pengtao DOU Xiying LI Minjuan 《中国地震研究》2020,34(2):210-218
To study the crustal movement in the vicinity of the epicenter before the Zhangye MS5.0 earthquake in 2019, the characteristics of crustal deformation before the earthquake are discussed through the GPS velocity field analysis based on the CMONOC data observed from GPS. The baseline time series between two continuous GPS stations and the strain time series of an area among several stations are analyzed in the epicenter area. The resulting time series of baseline azimuth around the epicenter reflects that the energy of the fault in the northern margin of Qilian Mountain is accumulated continuously before 2017. Besides, the movement trend of azimuth slows down after 2017, indicating the stress accumulation on both sides of the seismogenic fault zone has reached a certain degree. The first shear strain and EW-direction linear strain in the epicentral area of the Zhangye MS5.0 earthquake remain steady after 2017, and the surface strain rate decreases gradually after 2016. It is illustrated that there is an obvious deformation loss at the epicentral region three years before the earthquake, indicating that a certain degree of strain energy is accumulated in this area before the earthquake. 相似文献
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2020年5月18日云南省巧家县发生M_S5.0地震,中国数字强震动台网的13个强震台站和昭通简易烈度计示范项目建设的43个烈度计台站,共接收168条强震动记录,经常规处理,绘制震区加速度峰值等值线图,并与云南地区常用地震动衰减关系进行对比,分析加速度峰值较大的几个台站频谱特性,计算地震动能量持时,讨论中小地震中高加速度峰值/低震害现象的成因,得到以下结论:(1) PGA等值线图形状较为平滑,其长轴呈NW—SE向展布;(2)云南地区常用地震动衰减关系预测值总体衰减趋势与观测值一致,但在近场(0—30 km)时,预测值基本偏小;(3)加速度峰值较大的几个台站记录主频集中在1—5 Hz;(4)小河镇台记录的能量持时较短,说明能量衰减较快,属脉冲型记录,不会对建筑物造成较大破坏。 相似文献
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2015年1月14日乐山金口河M5.0地震发生在历史地震强度较低的川南山区与四川盆地交界一带。基于四川区域地震台网的震相报告与波形资料,采用双差定位法对地震序列进行重新定位,同时,采用CAP波形反演方法及HASH方法反演了主震及序列中8次ML≥2.0地震的震源机制解。另外,利用Coulomb3计算了主震发生后库仑应力改变量,得到的结果如下:①重新定位结果显示,金口河M5.0地震位于(103.18°E,29.32°N),震源深度16.6km,略深于波形反演结果(12km)。序列分布在NNW向天全-荥经断裂和NE向西河-美姑断裂的交汇部位,余震序列在空间上呈NE向展布。②M5.0主震的机制解为节面Ⅰ:走向350°/倾角46°/滑动角107°,节面Ⅱ:走向146°/倾角47°/滑动角73°,表现为走向NW(NNW)、中等倾角的逆冲型运动方式。序列中其余8次ML≥2.0余震大多以走向NE的逆冲型地震为主,个别为走滑或正断层类型。主震和大部分余震的节面方向不一致,主震节面方向与余震长轴方向也不一致。③主震后库仑应力改变量显示,余震主要发生在主震引起的库仑破裂应力增加的区域。综合分析推测,NNW向天全-荥经断裂为本次地震主震的发震构造,倾向NE的机制解节面Ⅰ指出了该断裂的几何产状;M5.0主震发生后,立即触发了其旁侧的NE向西河-美姑断裂,并激发了多次余震。 相似文献