排水管网系统精细拓扑模型描述与实现

针对当前排水管网拓扑模型简单、拓扑分析能力较弱的现状,提出了一种管网拓扑精细模型,改进了节点单一问题,将节点细分为多类并提出相应的拓扑规则,使之更符合管线客观模型,有利于进行空间分析。在管线和节点数据结构设计中,采用十字链表作为管网数据结构,便于管线与节点双向分析,同时建立R树索引,实现对空间数据管理。结合镇江管网地理信息系统开发实验,实现了基于改进拓扑模型的空间分析算法,结果发现本文提出的拓扑模型结构简单,易于实现,能够模拟管线排水细节,提供更多基于水务模型的空间分析类型,数据访问效率较高。     

网格模型的文物缺损面积计算

针对缺损文物的修复问题,本文提出通过计算三角网格模型中的三维孔洞面积来统计缺损文物面积的算法:建立三角网格中的点、边和面之间的拓扑关系,根据网格边界性质提取网格模型中的边界边,将提取出的孔洞边界边顺序连接组成孔洞多边形,并利用三角网格拓扑属性来识别孔洞边界和模型本身的外边界,通过坐标法计算三维孔洞多边形面积来统计文物缺损面积。应用实例表明,此方法用于孔洞边界的提取和缺损文物面积的计算是有效、可行的。     

基于排水管网GIS系统开展修补测提高数据现势性

排水管网属于地下隐蔽设施,具有变化慢、变化隐蔽两大特点,因此其空间数据主体相对稳定,而改造节点、更新数据困难。本文通过排水管网GIS系统收集管网管理一线工作人员存在的数据问题,针对这些问题进行修补测及成果确认,从而快速修复管线,完善排水管网数据库,提高数据现势性。     

利用高斯投影地图反算图斑椭球面积的误差

对于调查中计算图斑的椭球面积误差,普遍认为边长是造成最终面积误差的主要因素,本文指出了存在问题。使用不同节点分配的图斑数据,本文对国土调查面积公式的计算误差进行了分析,提出了图斑节点对称性是影响面积公式误差主要因素的观点,通过几何推证和实例说明了该结论的正确性。根据图斑节点对称性,提出了面积误差影响因子概念,给出了对图斑进行误差预判和按规定精度控制面积误差的方法,该方法对图斑具有通用性。通过编程验证了该方法的可操作性和实用性。     

椭球面上多边形的面积计算

本文给出了椭球面上多边形面积计算的一种新方法。这个多边形的边可能是大地线、恒向线、大圆线或是它们的组合。     

基于SWMM的管网排水模型研究

城市排水管网作为城市重要的基础设施,担负着城市生活污水、工业废水的收集以及雨水排除的任务.本文以哈尔滨市道里区某一条街道为研究对象,在SWMM模型的基础上建立了管网排水模型,根据一年一遇的强降雨数据进行模拟计算,得到溢流节点共20个,溢流峰值流量在0.009—0.272 m3/s之间,溢流总量在9—318 m3之间;瓶颈管段有50个,其中管段承载时长在0.09—1.74 h之间.最后针对构建的模型进行评估,总结出SWMM管网排水模型原理、模拟过程并给出了实施建议.     

面向防汛应急的重庆市洼地积涝预测模型研究

针对重庆市城市防汛应急的需要,在传统内涝模型的基础上,结合GIS空间分析与可视化技术,建立了一个运算快、实时性强的洼地积涝分析预测模型.该模型首先利用空间分析方法建立数字高程模型、城市洼地模型等数据模型,然后采用数值计算方法构建降雨模型、汇水模型、排水模型等分析模型,最后预测出洼地的积水面积、深度等.采用重庆市主城区的...     

排水管网多尺度几何语义建模

针对排水管网管理业务的不同需求,研究排水管网的多尺度几何语义表达方法,本文提出了包含“源—网—厂—汇”的排水管网多尺度几何语义模型。通过对排水管网组成要素及其相互间的关系进行系统地分析,并参考CityGML规范,提出了排水管网多细节层次模型;根据不同排水管网要素的特点,采用符号化及BIM等建模方式分别构建了不同细节层次的排水管网要素模型,支持排水管网多尺度可视化分析。并以某区域的排水管网数据为例,对本文提出的建模方法进行验证。     

排水管网数据采集方法的探讨

排水管网的规划、建设和科学管理对保障城市水环境安全具有十分重要的意义,而全面、准确、可靠的排水管网数据,是排水管网规划和管理的基础。结合上海市排水管网测绘项目的实践经验,介绍了排水管网数据采集的内容、特性、流程及质量检查的方法,为城市排水管网系统信息化建设提供了可靠的数据支持。     

排水管网GIS数据入库与更新方法研究

在对排水管网数据特征进行分析的基础上,设计了其在排水管网GIS系统中的入库算法,对新测管网数据和历史管网数据先进行缓冲区分析确定数据更新范围,然后叠加分析并进行相应数据编辑,实现排水管网GIS系统中数据的更新。     

基于分形的数字水系集水面积阈值确定研究

集水面积阈值的确定是基于数字高程模型提取水系过程中的关键环节,但目前集水面积阈值的确定存在着随意性和主观性.本文引入分维数量化集水面积阈值对水系提取的影响.研究表明,在给定标度区间内集水面积阈值和分维数存在着良好的回归关系.二者的拟合方程可用来进行集水面积阈值的合理确定和水系的准确提取.     

基于ArcEngine的排水管网数据处理系统开发

利用Arc Engine技术开发完成了排水管网数据处理系统,通过双录入技术实现了排水管网外业调查属性的录入和检查,实现了排水管网图形和属性录入功能,解决了多用户同时编辑时的冲突问题.通过定制的属性录入和图形编辑界面,使存储在ArcSDE中的空间数据以及SQL Server中的属性数据可以同时更新,并保持逻辑一致性.     

Catchment storm runoff modelling using the geomorphologic instantaneous unit hydrograph

A lumped empirical model, the Geomorphologic Instantaneous Unit Hydrograph (GIUH) rainfall-runoff model, is developed for the Can Le catchment in the upstream region of the Sai Gon river Basin (Vietnam). This model can serve to simulate catchment runoff into the Dau Tieng Reservoir and can be used as a flood forecasting tool for the ungauged Can Le catchment. The GIUH couples geomorphology and hydrology quantitatively. The obtained Unit Hydrograph is based on Horton's morphometric parameters; bifurcation, length and area ratios. A new functionality within the ILWIS GIS-RS package, namely ‘DEM-hydro processing’, is applied to effectively process a Digital Elevation Model to extract these ratios from the drainage network. To supplement the limited field data available, various satellites images have been used such as ASTER, SRTM (Shuttle Radar Topography Mission) and METEOSAT 5. A short field campaign to collect missing ground data was executed between September and October 2005. The data collected included discharge (and stage – discharge curve), meteorological data, soil, land use information that are used for paramerisation, calibration and validation of the GIUH. The model was successfully applied for the Can Le catchment. Using the Horton's morphometric parameters derived from the DEM with estimated overland and stream flow velocities, the model is easy-to-use.     

The adjustment of intersecting chains of triangulation using a medium sized computer

Summary A method of adjusting intersecting chains of triangulation by direct elimination, using variation of coordinates, is given which is suitable for a computer of modest storage capacity (about 4000–8000 words). The network is considered to be made up of “nodes” (where two or more chains intersect) and “links” (which connect two nodes). The normal equations for the points in each link are computed, the coefficients of the unknown coordinates of the associated nodes being treated as extra right hand sides. These equations are then solved to express the coordinates of any link station in terms of the associated nodes. The process is repeated for all links. The normal equations for the nodes are then set up and substitution made for all “link” terms. The method is particularly powerful for the adjustment of a few, long, interconnected chains since the reduced normal equation coefficients are then banded about the diagonal, the semi band width being a little greater than the largest number of unknowns in the nodes along a single tie chain. For a computer of 7000 words capacity it is possible to solve for a band width of over 100 unknowns i.e. more than 50 stations.     

基于数字高程模型的混合流向算法

从数字高程模型提取的汇水网络和汇水区等信息是分布式水文模型及应用分析的基础参数,基于地表汇水模拟的算法是提取该类信息的主要方法,其中,水流方向的确定对提取结果有着直接的影响。单流向算法因其易于实现、易于确定上游汇水区等特性,得到了广泛应用,然而单流向算法在坡度平缓区域会产生不自然的平行径流,能模拟地表水流分散径流特点的多流向算法可以在一定程度上避免此问题,但多流向算法使得不同区域的汇水单元可能存在交叉。本文结合两类流向算法各自的优点和适用性,设计实现了一种混合流向算法,以期在不同的地形条件下模拟得到更加合理的水流分配。首先,使用基于模板的形态检测方法,在给定阈值的基础上,对数字地形进行了分类,DEM被划分为山谷、山脊、鞍部、缓坡和陡坡5类。对陡坡、山谷和山脊区域运用单流向算法;对缓坡和鞍部区域采用多流向算法确定径流方向并进行水量分配。本文选取了黄土地貌和中低山丘陵的两个流域作为研究区,利用并采用了30 m和90 m两个分辨率的DEM。本文研究将混合流向算法与现有其他算法的结果进行比较。相比于多流向算法,该算法结果中的分散效应受到明显的抑制,相比于单流向算法,非自然的平行径流也大幅减少。同时,混合流向算法在较大分辨率DEM上（30 m）改进效果更加明显。     

A terrain evaluation using remote sensing and gis—case study of neyyar wild life sanctuary, Kerala

A multi-thematic analysis based on different physical factors has been adopted to generate integrated maps on erosion proneness as well as on critical slope under a GIS platform for terrain evaluation. This spatial data on erosion proneness has demonstrated that 17.62 km² area, out of the 140 km² in the catchment zone, needs careful attention for ecorestoration. In the critical slope map four land stability classes have been demarcated. The area represented as Unstable and Moderately-stable is found to be important for slope stability problems. Temporal change in the drainage network over a period of four decades as well as the extent of loss in the perennial status of tributaries have been recorded to evaluate the landform changes. A catchment treatment plan has also been suggested.     

排水管网数据采集和建库方法的研究

城市排水管网担负着收集和输送城市污水和降水的重要功能,对经济建设、人民生活、地表水和地下水的安全及生态环境有重大影响,是城市赖以生存和发展的物质基础,被称作城市的＂生命线＂和＂血脉＂。论文结合实际项目系统地阐述了排水管网数据采集、处理及建库的整个过程。实践证明,在该过程中用到的技术路线及实现方法是行之有效的。这些数据为建设＂数字水务、数字城市＂奠定了基础。     

Use of geomorphological units to improve drainage network extraction from a DEM : Comparison between automated extraction and photointerpretation methods in the Carraixet catchment (Valencia, Spain)

Drainage networks are one of the main elements characterizing basins, and network topology and geometry form the basis of many hydrological and geomorphological models (eg Geomorphological Unitary Hydrograph). The identification and manual delineation of channel networks from maps or aerial photographs requires much time and effort. In the last two decades, algorithms and procedures for automated extraction of drainage networks from digital elevation data have been developed and implemented in many specialized software applications. Nevertheless, automatically delineated channel networks do not always show close agreement with manually delineated networks. This paper describes a comparative analysis between a drainage network automatically extracted from a gridded digital elevation model, and the drainage network delineated manually from stereographic pairs of aerial photographs. The analysis showed that the automatic extraction technique may be adequate for catchment headwaters, but is inappropriate in the middle and lower basins, especially for alluvial fans and calcareous platforms. The paper suggests improving the automatic extraction technique by adapting it to operate with different parameters for each of the geomorphological units within the catchment.     

Integrating domain knowledge and graph convolutional neural networks to support river network selection

Deep learning is increasingly being used to improve the intelligence of map generalization. Vector-based map generalization, utilizing deep learning, is an important avenue for research. However, there are three questions: (1) transforming vector data into a deep learning data paradigm; (2) overcoming the limitation of the number of samples; and (3) determining whether existing knowledge can accelerate deep learning. To address these questions, taking river network selection as an example, this study presents a framework integrating hydrological knowledge into graph convolutional neural networks (GCNNs). This framework consists of the following steps: constructing a dual graph of river networks (DG_RN), extracting domain knowledge as node attributes of DG_RN, developing an architecture of GCNNs for the selection, and designing a fine-tuning rule to refine the GCNN results. Experiments show that our framework outperforms existing machine learning and traditional feature sorting methods using different datasets and achieves good morphological consistency after the selection. Furthermore, these results indicate that DG_RN meets the data paradigm of graph deep learning, and the framework integrating existing characteristics (i.e., Strahler coding, the number of tributaries, the distance between proximity rivers, and upstream drainage area) mitigates the dependence of GCNNs on plenty of samples and enhance its performance.