基于ε-SVR的滑坡位移预测

我国是一个地质灾害多发的国家,特别是滑坡发生的次数比较多、危害性比较大。因此对滑坡的位移进行监控预测有着十分重要的意义。对于滑坡位移变化的非线性问题,可以利用支持向量机在回归分析中的方法——ε-支持向量回归机（ε-SVR）进行预测,该方法基于统计学理论,在处理小样本、非线性、高维数等问题上有一定的优势。以福建八尺门滑坡的监测数据为例,将前面的17个位移数据作为学习样本,后面的6个位移数据作为预测样本,采用不同的核函数分别进行位移预测来与原始监测值进行对比,比较其预测精度。结果显示,该方法产生的预测值与原始监测值之间的误差比较小,其位移变化趋势与原始数据的变化趋势也基本一致,这说明该方法预测精度高,实用性强。     

基于GIS的兰州滑坡与泥石流灾害危险性分析

频繁发生的灾害愈来愈对人类社会造成巨大影响, 遥感、地理信息系统、全球定位系统和网络技术日益在减灾行动中发挥重要的作用. 兰州区域地质岩性、构造断裂、地震活动带、地表侵蚀强烈和地形起伏破碎等因素造成了兰州滑坡与泥石流灾害的发育, 夏季暴雨、人类工程活动等诱发下灾害频繁, 风险加剧. 针对兰州地区滑坡与泥石流等山地灾害对区域社会经济的影响, 通过建立区域滑坡与泥石流灾害的空间数据库, 在GIS技术辅助下实现了专家经验模型和Logistic模型对滑坡与泥石流灾害危险性的预测, 其中滑坡Logistic模型准确性达到85.3%, 专家经验模型准确性达到74.2%; 泥石流Logistic危险性预模型准确性达到80.5%, 专家经验模型准确性达到90.5%. 随着研究的深入, 综合遥感、地理信息系统、全球定位系统和网络技术的灾害研究与应用将在灾害防治中发挥重要的作用.     

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克隆植物准噶尔无叶豆的种群生物量结构及回归模型

根据根茎型克隆植物的特征,建立准噶尔无叶豆各构件单元生物量的模型。结果表明,准噶尔无叶豆的根茎生物量、根生物量、枝生物量和果实生物量分别是6.33、39.72、27.93 g·m^-2和3.47 g·m^-2。地上生物量所占比例为40.55%,小于地下生物量所占的比例59.45%。植株本身的高（H）、高与冠幅（C）的乘积（CH）与各部分生物量间的相关性很高;但C与各部分生物量间的相关性相对较低。在分析H、CH与各生物量相互关系的各种数学模型中,大多数模型有显著的相关性,选择其中相关显著性和R2值最高的模型,建立它们之间的相互关系模型。使用植株的CH所建立起来的线性数学模型对生物量的预测较好,为估算荒漠克隆灌木植物的生物量,提供个例物种的示范。     

基于广义回归神经网络的砂土液化综合判别方法

基于厦门地区大量钻孔试验数据,分别采用规范法和Seed法对该区饱和砂土进行液化判别。然后选取二者判别结果相同的数据作为训练和测试样本,运用广义回归神经网络,对二者判别结果分歧的钻孔数据进行二次判别。结果表明:广义回归神经网络性能良好,预测准确度高。此外,这种综合判别方法也提高了饱和砂土液化判别的准确度,并为其他地区饱和砂土的液化判别研究提供借鉴和参考。      

鄱阳湖区南昌县血吸虫疫情空间分布及 其流行因素分析

南昌县是鄱阳湖沿岸血吸虫流行区最主要的区域之一,是江西省血防工作的重点区域。结合疫水接触强度、村内耕牛数量、村到鄱阳湖边界的距离、村内湖岸线的长度等数据,通过传统线性回归、空间滞后回归模型、空间误差回归模型研究了2009年南昌县血吸虫疫情空间分布特点及其流行因素。结果表明：1）2009年南昌县血吸虫疫情主要分布在东北、东南部鄱阳湖沿岸、赣江流域沿岸等村域,疫水接触强度空间分布与疫情分布具有明显的一致性;2）单因素分析表明村内居民的疫水接触强度、村内耕牛数量、村内湖岸线长度、村到鄱阳湖边界的平均距离与病人数都具有明显的相关性,其中病人数和行政村到鄱阳湖沿岸的距离呈负相关性;3）3种回归模型拟合结果表明居民的疫水接触强度、村内耕牛数量、村到鄱阳湖边界的距离是影响南昌县血吸虫病人数分布的主要因子,以上3个指标可以解释总变异的81%。     

Micro-level analysis of childhood obesity, diet, physical activity, residential socioeconomic and social capital variables: where are the obesogenic environments in Leeds?

This paper describes global (whole of Leeds) and local (super output area) analyses of the relationship between childhood obesity and many 'obesogenic environment' variables, such as deprivation, urbanisation, access to local amenities, and perceived local safety, as well as dietary and physical activity behaviours. The analyses identify the covariates with the strongest relationships with obesity, and highlight variation in these relationships across Leeds, thus identifying 'at-risk' populations. This paper seeks to demonstrate the importance of analysis at the micro-level in order to provide health planners with additional information with which to tailor interventions and health policies to prevent childhood obesity.     

Land-cover and land-use change in a Mediterranean landscape: A spatial analysis of driving forces integrating biophysical and human factors

This article develops a spatial analysis applied to examine the main driving forces of land-cover and land-use (LCLU) change in a Mediterranean region. Three different tools have been used in order to differentiate LCLU changes, driving forces and landscape dynamics. LCLU changes have been quantified with remote sensing techniques, driving forces have been analysed with multiple logistic regressions combining biophysical and human variables, whereas landscape dynamics have been quantified using different metrics. Results show the intensification of subsidised herbaceous crops on the coastal agricultural plain, the abandonment of olive trees and vineyards in the transitional area and forest restoration in the mountainous subregion.     

Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression

This work has evaluated the probability of earthquake-triggered landslide occurrence in the whole of El Salvador, with a Geographic Information System (GIS) and a logistic regression model. Slope gradient, elevation, aspect, mean annual precipitation, lithology, land use, and terrain roughness are the predictor variables used to determine the dependent variable of occurrence or non-occurrence of landslides within an individual grid cell. The results illustrate the importance of terrain roughness and soil type as key factors within the model — using only these two variables the analysis returned a significance level of 89.4%. The results obtained from the model within the GIS were then used to produce a map of relative landslide susceptibility.