A comparative study of sequential minimal optimization-based support vector machines,vote feature intervals,and logistic regression in landslide susceptibility assessment using GIS

Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas.     

A comparative study of landslide susceptibility mapping using weight of evidence,logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin,China

The determining of landslide-prone areas in mountainous terrain is essential for land planning and hazard mitigation. In this paper, a comparative study using three statistical models including weight of evidence model (WoE), logistic regression model (LR) and support vector machine method (SVM) was undertaken in the Zhouqu to Wudu segment in the Bailong River Basin, Southern Gansu, China. Six conditionally independent environmental factors, elevation, slope, aspect, distance from fault, lithology and settlement density, were selected as the explanatory variables that may contribute to landslide occurrence based on principal component analysis (PCA) and Chi-square test. The relation between landslide distributions and these variables was analyzed using the three models and the results then used to calculate the landslide susceptibility (LS). The performance of the models was then evaluated using both the highly accurate deformation signals produced by using the Small Baseline Subset Interferometric Synthetic Aperture Radar technique and Receiver Operating Characteristic (ROC) curve. Results show more deformation points in areas with high and very high LS levels, and also more stable points in areas with low and very low LS levels for the SVM model. In addition, the SVM has larger area under the ROC curve. It indicates that the SVM has better prediction accuracy and classified ability. For the interpretability, the WoE derives the class of factors that most contributed to landsliding in the study area, and the LR reveals that factors including elevation, settlement density and distance from fault played major roles in landslide occurrence and distribution, whereas the SVM cannot provide relative weights for the variables. The outperformed SVM could be employed to determine potential landslide zones in the study area. Outcome of this research would provide preliminary basis for general land planning such as choosing new urban areas and infrastructure construction in the future, as well as for landslide hazard mitigation in Bailong River Basin.     

<Superscript>230</Superscript>Th/<Superscript>238</Superscript>U dating of hydrothermal sulfides from Duanqiao hydrothermal field,Southwest Indian Ridge

Duanqiao hydrothermal field is located between the Indomed and Gallieni fracture zones at the central volcano, at 50°28′E in the ultraslow-spreading Southwest Indian Ridge (SWIR). Twenty-eight subsamples from a relict chimney and massive sulfides were dated using the ²³⁰Th/²³⁸U method. Four main episodes of hydrothermal activity were determined according to the restricted results: 68.9–84.3, 43.9–48.4, 25.3–34.8, and 0.7–17.3 kyrs. Hydrothermal activity of Duanqiao probably started about 84.3 (±0.5) kyrs ago and ceased about 0.737 (±0.023) kyrs ago. The periodic character of hydrothermal activity may be related to the heat source provided by the interaction of local magmatism and tectonism. The estimated mean growth rate of the sulfide chimney is <0.02 mm/yr. This study is the first to estimate the growth rate of chimneys in the SWIR. The maximum age of the relict chimney in Duanqiao hydrothermal filed is close to that of the chimneys from Mt. Jourdanne (70 kyrs). The hydrothermal activity in Dragon Flag field is much more recent than that of Duanqiao or Mt. Jourdanne fields. The massive sulfides are younger than the sulfides from other hydrothermal fields such as Rainbow, Sonne and Ashadze-2. The preliminarily estimated reserves of sulfide ores of Duanqiao are approximately 0.5–2.9 million tons.     

结合宏观和微观视角的东莞城市建成区扩张特征分析

分析城市扩张过程中土地的供给现状、集约应用和利用监测等,对城市精准化管理与未来发展规划具有重要意义.本文基于东莞市历年遥感影像数据,提取城市用地信息,分别从宏观和微观角度量化城市用地变化和城市扩张过程,解释城市建成区扩张特征及城市扩张时空演化规律.研究表明:宏观上东莞市建城区面积1998-2018年逐渐扩张,直至201...     

2013年四川芦山7.0级地震烈度遥感评估

2013年4月20日四川芦山MS7.0级地震发生后,在灾区应急获取了多种高分辨率航空和无人机遥感影像,并快速解译提取了灾区建筑物震害信息.采用地震烈度遥感定量评估方法,利用2008年汶川8.0级地震等震后震害遥感解译和现场调查研究确定的经验震害遥感定量评估模型,获得了芦山地震灾区126个主要居民点的地震烈度遥感评估结果,并据此圈画了地震烈度分布遥感评估图.结果显示,本次地震Ⅸ度区面积约150km2,Ⅷ度区面积约900km2.该结果在第一时间(4月21日晚)提供给了中国地震局地震现场应急指挥部.对比分析显示,地震烈度遥感快速评估结果与中国地震局4月25日公布的地震烈度图,以及与笔者在现场实地进行的建筑物震害详细调查结果基础上评定的地震烈度具有较高的一致性.表明强烈地震发生后,借助于快速获取的灾区高分辨率遥感影像,可以快速估计地震烈度分布,对地震灾区灾情估计和抗震救灾工作具有十分重要的参考意义.     

不同仪器烈度算法在四川地区 历次地震中的比较应用

强震观测资料包括强震动时程记录资料和宏观震害调查资料两大类, 前者是定量的微观数据, 后者则是定性的宏观指标. 本文明晰给出了微观仪器烈度与宏观仪器烈度、 微观考察烈度与宏观考察烈度的概念, 并在此基础上提出了只有在宏观仪器烈度与宏观考察烈度之间进行比较才具有实质性的意义;同时对国内外常见的仪器烈度算法进行了比较性研究, 得出了袁一凡仪器烈度算法可靠性更高的结论;最后以四川地区历年来重要震例的强震动记录为依据, 对修正的袁一凡仪器烈度算法的可靠性进行了比较应用. 结果表明, 对袁一凡仪器烈度算法修正与扩展的应用是可行的, 同时也验证了将微观仪器烈度与宏观考察烈度直接进行比较会存在较大的差距.      

合肥地震台钻孔体应变干扰分析

分析合肥地震台钻孔体应变观测资料干扰因素及特征,发现水位和气压等因素对TJ-Ⅱ型钻孔应变仪影响显著.在该地震台观测优化改造期间,钻孔体应变受到临近钻孔灌水的影响,而降雨和灌水导致的水位变化,对体应变干扰程度不同.     

水化硅酸钙动态力学性能的分子动力学模拟研究

对水泥主要水化产物——水化硅酸钙的力学特性进行了分子动力学模拟。采用Hamid 1.1纳米托贝莫来石作为水化硅酸钙的初始结构,通过分子动力学模拟,获得水化硅酸钙的拉伸和压缩应力应变关系,并考虑了应变率和原子数对力学特性的影响。分子动力学模拟研究发现,计算峰值应力、峰值应变都较宏观试验结果高出很多;拉伸峰值应力较压缩峰值应力要小;峰值应力、峰值应变和弹性模量随着应变率的增加而增加,随着模拟晶胞体积（原子数）的增加而降低。研究表明采用分子动力学模拟能获得原子尺度水化硅酸钙动态力学性能,为揭示水泥基材料动态力学性能微观机理奠定了基础。     

基于能量平衡的多年冻土区公路设计理论研究

青藏公路长期研究表明,青藏高原多年冻土公路工程空间效应敏感,主要表现为公路空间效应直接改变下伏冻土地基的天然能量平衡状态,继而引发一系列工程病害. 针对这一工程问题,提出多年冻土地区公路能量平衡设计理论,研究公路工程建设引发的多年冻土地基能量变化状态,平衡自然环境变化和工程建设等导致的外界“有害”能量导入与工程处置措施对冻土地基中“有害”能量导出之间相互关系,从空间和时间两个维度分析多年冻土地区公路工程的能量平衡过程. 据此,作为多年冻土公路工程的设计依据,将为青藏高原高速公路的科学设计提供理论支持.     

2013年西藏嘉黎县“7.5”冰湖溃决洪水成因及潜在危害

冰湖溃决洪水（泥石流）是西藏自治区主要自然灾害之一. 2013年7月5日,西藏自治区嘉黎县忠玉乡发生“7.5”冰湖溃决洪水灾害事件,导致人员失踪,房屋被毁,桥梁、道路等基础设施遭到严重破坏,直接经济损失高达2.7亿元. 基于不同时间段地形图和遥感影像资料,利用地理信息技术,发现导致“7.5”洪灾的溃决冰湖为然则日阿错. 该冰湖溃决的直接诱因可能是雪崩和冰崩的共同作用,溃决前的强降水过程及气温的快速上升是其间接原因,而冰湖长期稳定的扩张导致水量聚集是其溃决并造成巨大灾害的基础. 然则日阿错溃决后形成2个冰湖,面积分别为0.25 km²和0.01 km²,再次发生溃决的概率极小. 这次溃决洪水和泥石流灾害事件阻塞了尼都藏布的罗琼沟及衣布沟,并形成2处面积分别为0.33 km²和0.13 km²堰塞湖,且存在溃决风险,在今后一段时间内应加强监测工作与排险工程实施.