基于风险评估模型的环境风险地图研究

环境风险地图基于地理信息系统(GIS)对信息和数据的空间分析,通过各种风险评估或预测模型,将评估的结果以空间方式展示出来.由于环境风险地图直观、信息量大的优点,有助于环境风险管理以及突发事件的应急反应和实时决策.对风险地图种类作了概述,总结风险制图中需注意的问题以及国内外风险地图的应用研究以及发展情况.     

滑坡灾害危险性评价信息图谱研究——以福建省莆田市为例

近年来,滑坡灾害频繁发生且极大地危害着人类的生产生活,因此,如何快速有效地进行滑坡灾害危险性分析与评价,进而为滑坡的预测、预报及风险管理服务是一个亟待解决的问题。以莆田市为例,通过将地学信息图谱应用到滑坡灾害危险性评价中,构建莆田市高程、岩性、坡度、坡向影响因子信息图谱,并在此基础上得出莆田市滑坡灾害危险性评价信息图谱,以一系列的图谱直观地反映了莆田市滑坡发生的机制及滑坡危险性空间分布情况,反演了莆田市滑坡发生的时空变化规律,为滑坡灾害预测提供了矢量化的图形信息。     

A mathematical structure for analyzing maps

The growing use of computers in environmental management is profoundly changing data collection procedures, analytic processes, and even the decision-making environment itself. The emerging technology of geographic information systems (GIS) is expanding this revolution to integrate spatial information fully into research, planning, and management of land. In one sense, this technology is similar to conventional map processing involving traditional maps and drafting aids, such as pens, rub-on shading, rulers, planimeters, dot grids, and acetate sheets for light-table overlays. In another sense, these systems provide advanced analytic capabilities, enabling managers to address complex issues in entirely new ways. This report discusses a fundamental approach to computer-assisted map analysis that treats entire maps as variables. The set of analytic procedures for processing mapped data forms a mathematical structure analogous to traditional statistics and algebra. All of the procedures discussed are available for personal computer environments.     

鄱阳湖地区洪水灾害模式分析

在对多年水文数据进行分析的基础上，利用Kohonen自组织映射对鄱阳湖地区降水时序变化、长江来水和湖水水位变化进行模式识别。之后，将所得的模式作为解释变量，用CART方法对样本年份进行分类，建立了鄱阳湖区灾害模式判别树。借此揭示了鄱阳湖地区降水和长江来水对鄱阳湖洪涝灾害的影响。     

Estimating the risk of insect species invasion: Kohonen self-organising maps versus k-means clustering

Previous work on the estimation of the invasiveness of insect pest species used a single Kohonen self-organising map (SOM) to quantify the invasion potential of each member of a set of species in relation to a particular geographic region. In this paper that method is critically compared to an alternative approach of calculating the invasive potential of insect pest species as an outcome of clustering of regional species assemblages. Data clustering was performed using SOM and k-means optimisation clustering and multiple trials were performed with each algorithm. The outcomes of these two approaches were evaluated and compared to the previously published results obtained from a single SOM. The results show firstly, due to the inherent variation between trials of the algorithms used, that multiple trials are necessary to determine reliable risk ratings, and secondly, that k-means clustering can be considered a more appropriate algorithm for this particular application, as it produces clusters of higher quality, as determined by objective cluster measures, and is far more computationally efficient than SOM.     

基于混乱图的CI系统应急能力不足挖掘方法

关键基础设施(Critical Infrastructure, CI)系统应急能力建设的重要性是不言而喻的,已有的应急能力研究主要是基于应急能力评价指标体系展开评估,以此识别应急准备问题,但并未充分认知应急准备混乱,难以有效地挖掘应急能力不足。基于此,面向CI系统,从应急部门的角度采用混乱图挖掘应急能力不足,阐述特定突发事件情景下CI系统应急能力不足混乱图的构建流程。通过案例分析表明:CI系统应急能力不足混乱图可有效地反映应急准备混乱,并指导相关应急部门提升其应急能力。     

尾矿坝浸润线时空混沌及其安全机理

在单向耦合映象格子模型基础上,考虑尾矿坝坡角因素,建立了浸润线耦合映象格子模型vn 1(i)=(1-ε)f(vn(i)) ε(1 sinθ)f(vn(i-1)).根据浸润线的初值条件和边界条件,对该模型进行了非线性动力学分析.结果表明,浸润线演化存在复杂时空混沌行为,可分为随机图样流、选择图样流、缺陷传播行为及完全发展湍流4种.非线性强度α和耦合强度ε的取值范围较小, 且ε较小时就可以得到非常丰富的动力学图案.浸润线演化受坡角影响比较明显,坡角越大越容易出现混沌状态.浸润线的各种时空行为反映了其相应的客观状态,从而揭示了浸润线的安全机理.浸润线耦合映象格子模型也可以用来解释其他各类边坡的复杂渗流现象.     

基于注意力机制的人群计数方法

为准确预测固定场景中的人群计数,在人群分析领域,采用一种融合注意力机制的卷积神经网络(CNN)进行人群计数,该模块结合空间域注意力和通道域注意力,空间域注意力可以编码整个图像的像素级上下文信息,以更准确地表达像素级别的密度图,而通道域注意力可以在不同的通道中提取更多的区分特征使网络显著表达人群的局部区域,并在多个公开数...     

Spatial interpolation of air temperature using environmental context: Application to a crop model

The air temperature is one of the main input data in models for water balance monitoring or crop models for yield prediction. The different phenological stages of plant growth are generally defined according to cumulated air temperature from the sowing date. When these crop models are used at the regional scale, the meteorological stations providing input climatic data are not spatially dense enough or in a similar environment to reflect the crop local climate. Hence spatial interpolation methods must be used. Climatic data, particularly air temperature, are influenced by local environment. Measurements show that the air above dry surfaces is warmer than above wet areas. We propose a method taking into account the environment of the meteorological stations in order to improve spatial interpolation of air temperature. The aim of this study is to assess the impact of these corrected climatic data in crop models. The proposed method is an external drift kriging where the Kriging system is modified to correct local environment effects. The environment of the meteorological stations was characterized using a land use map summarized in a small number of classes considered as a factor influencing local temperature. This method was applied to a region in south-east France (150×250 km) where daily temperatures were measured on 150 weather stations for two years. Environment classes were extracted from the CORINE Landcover map obtained from remote sensing data. Categorical external drift kriging was compared to ordinary kriging by a cross validation study. The gain in precision was assessed for different environment classes and for summer days. We then performed a sensitivity study of air temperature with the crop model STICS. The influence of interpolation corrections on the main outputs as yield or harvest date is discussed. We showed that the method works well for air temperature in summer and can lead to significant correction for yield prediction. For example, we observed by cross validation a bias reduction of 0.5 to 1.0°C (exceptionally 2.5°C for some class), which corresponds to differences in yield prediction from 0.6 to 1.5 t/ha.