辽宁省基本公共服务均等化水平时空格局研究

通过构建基本公共服务均等化水平评价指标体系,采用集对分析法对2003—2013年辽宁省14个城市的基本公共服务水平进行测度,并对其时空格局进行分析。研究表明:自2003年实施东北振兴战略以来,辽宁省的基本公共服务均等化水平得到较大幅度提高,总体上呈由低水平向高水平演进的趋势,并具有明显的阶段性特征;基本公共服务的市际间绝对差异和相对差异均呈缩小趋势,低水平地区的"赶超效应"明显。空间上呈现出"中部凸起、两翼塌陷"的格局。各类型城市中,中心城市基本公共服务水平最高,资源型城市次之,一般地级市水平最差。     

The statistics of particle velocities and accelerations under waves with directional spread

The statistics of the horizontal component of the water particle velocity vector under random waves are considered. A spread in the directions of travel of the component wave trains does not affect the rms value of the modulus r but it affects the shape of its probability distribution in such a way that the pr3bability of extreme values is reduced. For small and moderate directional spreads the modulus for a given probability of exceedance is reduced by a factor which tends to F_s as the probability of exceedance tends to zero, where F_s is the spread factor (which is typically 0.9 in temperate storms). However, the convergence on this asymptote is not complete for probabilities of engineering importance, particularly for wide directional spreads. The standard deviation of the probability distribution of the direction of the vector is reduced roughly in inverse proportion to r. The same conclusions apply to particle accelerations.     

地图图像系统为布尔代数系的证明

根据布尔代数的定义和充要条件,论证了地图图像系统为布尔代数系,从而为地图编绘自动化提供了理论基础和数学工具。     

基于改进型BP网络的空间电场分类研究

空间电场信号异常识别是研究地震引起电离层扰动的重要内容。 将空间超低频电场电位数据看作随机数字信号, 以均值、 均方差、 偏度和峰度等四个指标进行描述, 采用“5·12”汶川大地震前空间超低频电场电位数据作为原始数据, 训练改进型BP神经网络, 建立了空间电场信号异常分类识别模型, 并以SOM神经网络进行验证。 计算结果显示, 空间超低频电场电位异常信号主要集中在5°~25°N, 88°~120°E之间的区域, 汶川大地震影响范围内的电离层扰动, 可能是汶川地震发生前引起的, 这与前人研究一致, 说明采用改进型BP神经网络异常分类识别模型研究地震引起的电离层扰动是可行的。     

应急演练中基于G-R关系的主余型地震余震震级模拟

在现有的日常地震演练过程中,与应急救援密切相关的地震余震信息产品较为缺乏,直接影响发震构造的判断以及影响场修正等关键环节。本文从计算机系统提供的均匀分布随机数出发,运用反函数法模拟生成余震序列,并进行系统检验,证实该方法产生的余震序列满足G-R频次关系。模拟生成的余震震级数据既有助于增强地震应急救援演练的现实性,也有助于丰富地震应急宣传产品,提升地震部门的履职能力。     

Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours

To present a new method for building boundary detection and extraction based on the active contour model, is the main objective of this research. Classical models of this type are associated with several shortcomings; they require extensive initialization, they are sensitive to noise, and adjustment issues often become problematic with complex images. In this research a new model of active contours has been proposed that is optimized for the automatic building extraction. This new active contour model, in comparison to the classical ones, can detect and extract the building boundaries more accurately, and is capable of avoiding detection of the boundaries of features in the neighborhood of buildings such as streets and trees. Finally, the detected building boundaries are generalized to obtain a regular shape for building boundaries. Tests with our proposed model demonstrate excellent accuracy in terms of building boundary extraction. However, due to the radiometric similarity between building roofs and the image background, our system fails to recognize a few buildings.     

一种基于高斯马尔可夫随机场的异常目标探测方法

利用高斯马尔可夫随机场模型描述像元的邻域相关性信息,并将这种邻域信息引入到局域异常探测器中,提出了一种顾及邻域信息的高光谱遥感影像局域异常目标探测算法。实验证明,该方法克服了传统异常探测方法仅仅利用光谱信息的不足,比经典的RX算法的探测效果更好,并且可以更有效地探测出大于一个像元的异常目标。     

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.     

Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process

This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized linear model (GLM), stepwise generalized linear model (SGLM), elastic net (ENET), partial least square (PLS), ridge regression, support vector machine (SVM), classification and regression trees (CART), bagged CART, and random forest (RF) for gully erosion susceptibility mapping (GESM) in Iran. The location of 462 previously existing gully erosion sites were mapped through widespread field investigations, of which 70% (323) and 30% (139) of observations were arbitrarily divided for algorithm calibration and validation. Twelve controlling factors for gully erosion, namely, soil texture, annual mean rainfall, digital elevation model (DEM), drainage density, slope, lithology, topographic wetness index (TWI), distance from rivers, aspect, distance from roads, plan curvature, and profile curvature were ranked in terms of their importance using each MLA. The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE (root mean square error), MAE (mean absolute error), and R-squared. Based on the comparisons among MLA, the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared, and was therefore selected as the best model. The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance. According to the GESM generated using RF, most of the study area is predicted to have a low (53.72%) or moderate (29.65%) susceptibility to gully erosion, whereas only a small area is identified to have a high (12.56%) or very high (4.07%) susceptibility. The outcome generated by RF model is validated using the ROC (Receiver Operating Characteristics) curve approach, which returned an area under the curve (AUC) of 0.985, proving the excellent forecasting ability of the model. The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.     

1951—2017年热带气旋移动速度特征统计分析

热带气旋移动速度是目前热带气旋研究热点之一。本文利用1951—2017年中国国家气象局最佳路径集资料,系统分析了西北太平洋海域热带气旋不同强度等级移动速度的年代际特征,初步探讨其原因,并对不同强度等级移动速度的分布特征进行系统诊断。结果表明,1951—2017年间平均移动速度(所有强度等级平均及对于大部分强度等级)呈现出减慢趋势,且主要体现在强热带气旋等级移动速度减慢;热带气旋向高纬度迁移趋势理论上将使移动速度增大而移动速度仍然减慢,说明气候因素是引起移动速度减慢的一个重要因素;不同强度等级平均移动速度、增强阶段基本相当,减弱阶段随强度减弱在初、中期明显增加,之后又逐渐减小,变性气旋移动速度显著增加;不同强度等级速度概率分布,增强阶段基本相同且相对集中,减弱阶段差异较大且相对分散,变性气旋最为分散。