The Application of Artificial Neural Networks to Landslide Susceptibility Mapping at Janghung, Korea

The purpose of this study was to develop techniques for landslide susceptibility using artificial neural networks and then to apply these to the selected study area at Janghung in Korea. Landslide locations were identified from interpretation of satellite images and field survey data, and a spatial database of the topography, soil, forest, and land use. Thirteen landslide-related factors were extracted from the spatial database. These factors were then used with an artificial neural network to analyze landslide susceptibility. Each factor's weight was determined by the back-propagation training method. Five different training sets were applied to analyze and verify the effect of training. Then the landslide susceptibility indices were calculated using the back-propagation weights, and susceptibility maps were constructed from Geographic Information System (GIS) data for the five cases. Landslide locations were used to verify results of the landslide susceptibility maps and to compare them. The artificial neural network proved to be an effective tool for analyzing landslide susceptibility.     

基于计算机X射线断层术与扫描电镜图像的黄土微结构定量分析

以湿陷性黄土的电镜扫描（SEM）和三轴CT扫描试验为基础,针对CT图像分辨率较低、难以实现土微结构精确量化的缺陷,通过对不同放大倍数的SEM图像进行图像分析,并从其中选择标准训练样本,利用训练样本对CT图像进行监督分类,从而达到定量化分析土的微结构的目的。通过比较CT图像基于自身灰度分级和基于SEM训练样本两种不同方法进行监督分类,结果表明基于SEM训练样本的CT图像监督分类,可以更好地量化监测黄土大孔隙、团粒、黏土集粒和矿物颗粒在固结剪切过程中的变化规律,从而为土的微结构研究提供了新的视角。     

2018年北京“7.16”暖区特大暴雨特征及形成机制研究

利用京津冀区域加密自动气象站、SA多普勒天气雷达、L波段风廓线雷达、NCEP 0.25°再分析资料及0.03°高分辨率地形资料研究了北京2018年7月15—16日暖区特大暴雨特征和形成机制。结果表明:(1)这次暖区特大暴雨发生在副热带高压边缘的暖气团(θ_se高能区)中,无明显冷空气强迫,斜压性弱,有丰沛的水汽,850 hPa以下出现强水汽辐合。(2)暴雨的中尺度对流系统发展有3个过程:带状对流建立和局地强雨团影响、北京北部"列车效应"南部雷暴冷池出流造成对流加强和移动、平原地区线状对流重建。(3)暴雨发生前,低层西南风出现风速脉动,低空急流建立。首先在2500—3500 m高度形成低空急流,2 h后2500 m以下风速显著增大,5 h后急流厚度由边界层伸展到700 hPa。急流出口区降压,低层出现气旋性风场或切变,有利于垂直上升运动发展,触发和加强对流。(4)西南低空急流暖湿输送导致高温、高湿、高能的对流不稳定层结反复重建,这是对流发展加强的重要原因。(5)地面辐合线是对流触发并逐渐组织成带状对流系统的关键影响因素。地面辐合线方向、低空急流轴、回波移动方向三者几乎...     

云雾覆盖下地表温度重建机器学习模型的训练集敏感性分析

热红外遥感是监测地表温度的重要技术手段.然而,由于其易受云雾影响,热红外遥感反演地表温度存在大量的观测空白区域,严重影响地表温度产品的应用.近年来,机器学习算法的发展为实现地表温度无缝观测提供了新的技术手段.然而,基于机器学习的云雾覆盖像元重建方法与训练样本的数量和分布有着直接的联系,其应用条件在现有研究中还鲜有讨论....