Cartographic Forecasting of Special Medical-Ecological Problems

A group of Siberian researchers addresses a range of issues surrounding efforts to apply public health information derived from medical-ecological monitoring and mapping to the task of forecasting/prevention of hazardous medical-ecological situations. Some specific examples are provided from the authors' work in Altay Kray. Translated by Edward Torrey, Alexandria, Virginia from: Geografiya i prirodnyye resursy, 2002, No. 1, pp. 54-58.     

AVALANCHE-HAZARD MAPS FOR PLANNING PURPOSES

General principles governing a structured, multiscalar approach to avalanche-hazard mapping are outlined. At an initial [regional] stage, intermediate-scale mapping (1:300,000) reveals spatial patterns of relevant avalanche parameters and possible lines of research into avalanche forecasting and control. Mapping at the second [local-planning] and third [site-planning] stages, at scales of 1:100,000 and 1:10,000 respectively, is oriented toward formulation of specific site selection and protection measures, and the development of monitoring and forecasting procedures. Excerpts from representative maps at each level are provided, along with detailed legends. Translated from: Materialy glyatsiologicheskikh issledovaniy, USSR Geophysical Committee Publication No. 51. Moscow: Mezhduvedomstvenniy geofizicheskiy komitet, 1984, pp. 216-223.     

沉降监测数据处理软件系统的设计与开发

沉降监测在工程建设中起到至关重要的作用,随着监测仪器设备和测量手段的提高,对监测数据处理手段和预报准确度提出了更高的要求,如何将监测工作和预报决策流程一体化已成为亟待解决的问题。本文阐述了沉降监测数据处理软件系统的需求、特点、设计思想和主要模块功能,通过实例介绍了软件系统的操作步骤,实现了数据采集、平差处理、预报分析、灰色预测的有机集成。     

基于自适应卡尔曼滤波的地表移动变形预报研究

构建自适应卡尔曼滤波预报模型,利用GNSS CORS连续运行实时监测数据,通过自适应卡尔曼滤波预报值、标准卡尔曼滤波预报值及实测数据对比分析,得到自适应卡尔曼滤波预报偏差明显减小,预报精度明显提升,满足了地表移动变形实时监测的精度要求。      

长白山火山灾害信息系统的设计与实现

通过监测子系统、预测预报子系统和灾害评估子系统的设计，完成了长白山火山灾害信息系统的设计，并提出了利用遥感和地理信息系统技术对火山发进行监测、预测预报与灾害评估的技术方法。     

变权组合预测模型在大坝沉降监测中的应用

为探讨变权组合预测模型在变形监测中的应用,以监测某大坝沉降量为实例,研究基于线性回归预测模型和灰色模型GM(1,1)的变权组合。通过预测该大坝坝内沉降量,比较两种单一预测模型和变权组合预测模型的预测误差平方和,证明变权组合预测模型在沉降预测中具有更好的预测精度。     

季节指数及其在季节性监测资料分析和预报中的应用

基于灰色模型的诸多优点，作者选用GM(1，1)模型分析和预报形变监测序列。然而直接应用GM(1，1)灰色模型分析和预报具有季节性的监测序列时往往精度不高。因此，作者提出运用基于季节指数的“去季节波动”法与GM(1，1)混合建模，对监测资料进行分析与预报。基于均方差和平均绝对误差两个精度准则，作者对此方法与周期函数拟合模型进行了比较。结果表明，此方法提高了具有季节性波动监测序列的预报精度，且建模方法简便、快捷。     

Potential fish habitat mapping using MODIS-derived sea surface salinity,temperature and chlorophyll-a data: South China Sea Coastal areas,Malaysia

This paper demonstrates the use of moderate resolution imaging spectro-radiometer (MODIS) data for fish forecasting mapping of seasonal spatial distribution of sea surface salinity (SSS), temperature (SST) and chlorophyll-a in the ocean waters off the coast of Semporna, Malaysia. Multi-linear regression analysis was performed to estimate SSS and the Brown and Minnet algorithm was used for the SST. The extracted parameters were validated using in situ measurement taken with Hydro-Lab equipment. The extracted parameters from MODIS images reveal the signature values which establish the relationships between these parameters, and thus delineating the potential fish zonation (PFZ) map. These developed models will help for accurate monitoring of large coverage areas at low cost and within short period of time. Furthermore, such models will allow the prediction of the total fish catch in different seasons, thus contributing to fish industry management and marketing. This research recommends the use of PFZ map for mass scale fish harvesting in short time for larger areas. Finally, the research has developed a potential fish zone model amalgamating all the above parameters. The PFZ mapping was carried out off the coast of Semporna, Sabah as there were sufficient fish catch data for accuracy assessment. The R was computed as 0.93 and the higher fish catch areas have coincided very well with the higher PFZ values, meaning the tool is ready for use for operational near real-time fish forecasting.     

Monitoring canopy growth and grain yield of paddy rice in South Korea by using the GRAMI model and high spatial resolution imagery

Monitoring crop conditions and forecasting crop yields are both important for assessing crop production and for determining appropriate agricultural management practices; however, remote sensing is limited by the resolution, timing, and coverage of satellite images, and crop modeling is limited in its application at regional scales. To resolve these issues, the Gramineae (GRAMI)-rice model, which utilizes remote sensing data, was used in an effort to combine the complementary techniques of remote sensing and crop modeling. The model was then investigated for its capability to monitor canopy growth and estimate the grain yield of rice (Oryza sativa), at both the field and the regional scales, by using remote sensing images with high spatial resolution. The field scale investigation was performed using unmanned aerial vehicle (UAV) images, and the regional-scale investigation was performed using RapidEye satellite images. Simulated grain yields at the field scale were not significantly different (p = 0.45, p = 0.27, and p = 0.52) from the corresponding measured grain yields according to paired t-tests (α = 0.05). The model’s projections of grain yield at the regional scale represented the spatial grain yield variation of the corresponding field conditions to within ±1 standard deviation. Therefore, based on mapping the growth and grain yield of rice at both field and regional scales of interest within coverages of a UAV or the RapidEye satellite, our results demonstrate the applicability of the GRAMI-rice model to the monitoring and prediction of rice growth and grain yield at different spatial scales. In addition, the GRAMI-rice model is capable of reproducing seasonal variations in rice growth and grain yield at different spatial scales.     

GEOGRAPHIC FORECASTING AND MAPPING

The author outlines general principles and methods of predictive or forecast mapping in the USSR, more specifically the use of maps in support of geographic forecasting, i.e., prediction of relationships involved in matter, energy, and other flows of ecosystem components. Maps are employed in all stages of geographic forecasting, particularly to identify spatial patterns which can be extrapolated in space or time and used to predict distributions that are difficult or impossible to determine directly, as well as to graphically portray these expected distributions. A scheme for classifying forecast maps in terms of their orientation in time and space is presented. Translated from: Prognoznoye blogeograficheskoye kartografirovaniye: regional'nyy aspekt, Moscow: Nauka, 1985, pp. 6-24.     

An integrated geospatial information service system for disaster management in China

The Chinese Government and citizens face enormous challenges of disaster management as widespread devastation, economic damages, and loss of human lives caused by increasing natural disasters. Disaster management requires a complicated iterative process that includes disaster monitoring, early detection, forecasting, loss assessment, and efficient analysis of disaster reduction. Each task typically involves the use of technologists and multiple geospatial information resources, including sensors, data sources, models, geo-tools, software packages, and computing resources. However, most existing disaster management systems operate in a typical passive data-centric mode, where resources cannot be fully utilized. This impediment is partially being addressed by the increasingly complex application requirements and the growing availability of diverse resources. In this paper, we summarize and analyze the practical problems experienced by the National Disaster Reduction Application System of China. To address the issues of data-centric, centralized, isolated solutions, we propose a novel Focusing Service Mechanism, which is capable of scheduling and allocating for optimum utilization of multiple resources, to dynamically generate collaborative and on-demand disaster information services. We also demonstrate the design and implementation of the Integrated Disaster Information Service System (IDISS). Through the service strategies of Virtualizing, Wrapping, and Integrating, disaster-related resources are constructed into services in the IDISS. These services are dynamically aggregated into focusing service chains, for diverse disaster management tasks. Actual applications illustrate that the proposed service system can significantly improve the capability of disaster management in China.     

城市地铁变形监测数据处理

为确保地铁施工安全进行变形监测是非常重要的手段,而变形监测的最终目的是预测,即对观测物的未来形态进行预报和分析。时间序列的原理是找出事物随时间变化的规律,从而对数据变化趋势做出正确的分析和预报。本文通过对时间序列的研究对长春地铁繁荣路站的变形监测数据进行了分析和预报。     

Elman神经网络在变形预报中的应用研究

神经网络作为一门快速发展起来的非线性科学,在处理一些背景不清楚而且极其复杂信息的时候,就会显示出其独特的优越性。本文通过Elman神经网络应用到滑坡变形监测中,建立预报模型,并以Matlab神经网络工具箱进行程序设计,最后运用到具体实例中,通过模型的预报精度,来验证Elman神经网络模型在滑坡监测预报中的可行性。     

基于小波变换与ARMA模型的建筑物沉降预报

对建筑物进行沉降监测并预报其变化趋势,能有效保障建筑物的安全性。本文提出一种基于小波变换的ARMA模型用于建筑物沉降预报。利用小波多尺度分析将沉降监测数据分解为高频信号和低频信号,并分别采用ARMA模型进行预测,然后将各序列预测结果进行合成,得到最终预测结果。并以青岛市某高层建筑物监测数据为例,分别采用传统ARMA模型以及基于小波变换的ARMA模型进行预报对比分析,结果表明基于小波变换的ARMA模型取得了较高的预报精度。     

GNSS自动化监测系统的大坝变形预测方法研究

以GNSS自动化监测系统的大坝变形预测方法为主要研究目的,针对大坝GNSS自动化监测数据大样本、高采样率、连续等特点,提出了一种结合小波分析与BP、NAR神经网络预测大坝变形的新方法。利用多尺度小波分析对GNSS大坝变形数据序列进行分解与重构,对重构后的低频近似序列采用BP神经网络进行建模预测,对重构后的高频细节序列采取NAR动态神经网络进行建模预测,最后叠加各尺度下预测结果获得大坝变形预测值。应用结果表明,该方法预测精度高、泛化性能好,可广泛应用于采用GNSS自动化监测系统的大坝变形预测。     

灰色动态预测方法及其在变形预测中的应用

在分析比较了ＧＭ（１，１）预测模型的不足之后，提出一种实时引入新信息的等维新息和等维灰数递补组合动态预测方法。首先同常规预测法作了比较，然后进行了三峡链子崖危岩体变形发展趋势预测及有效性验证。     

指数平滑与曲线拟合模型在沉降监测中的应用

通过分析建筑物沉降监测数据的特点,采用了指数平滑和曲线拟合模型对建筑物沉降监测数据进行处理,实现了两种数据处理模型的建模过程,并用于建筑物沉降量的实际预测,验证了模型的可行性。     

工程建筑沉降预测的非等间距灰色建模

介绍了传统灰色GM(1,1)模型建立的基本思路及其精度评定方法,着重探讨了非等间距数列的灰色预测建模问题,给出了详细的建模过程。并结合高层建筑沉降监测实例进行分析,取得了较好效果。     

融机器学习与WRF大气模式的PM2.5预报方法

针对当前我国重污染天气实时的空气质量预报问题,该文提出了一种融合随机森林算法与WRF大气模式的PM2.5浓度实时预报方法。该方法结合了北京市地面空气质量监测数据和WRF气象数据进行分析,将高层大气状态(如逆温层高度等)融入了预报模型中,建立了0~72h的PM2.5浓度实时预报模型。实验证明,该模型能够对0~72h单站点的PM2.5浓度进行较高精度的实时预报,且在24~72h的长时预报结果上较基于地面空气污染物数据与地表气象站数据的预报方法精度有明显提升,即该方法可以更好地模拟大气物理化学状态,从而更为精准地进行长时PM2.5浓度预报。     

基于物联网与GIS的地下综合管廊环境监测系统

针对综合管廊环境监测需求,本文运用物联网、地理信息系统等技术,研究地下综合管廊环境监测系统,实现管廊环境监测和预警。该研究可为保证综合管廊的正常运行和提高突发事件的处理能力提供借鉴。