基于数字地形的三维道路快速建模与应用

介绍一种在高精度三维地形模型的基础上,通过导入道路规划设计快速生成三维道路模型,并进行土石方量计算等量化分析的方法。基于三维道路模型,可以开展各类可视化分析和规划管理工作,为非专业人士理解规划设计意图提供了直观的方式。最后,以典型示范应用为例,说明研究成果可应用的领域和效果。     

交通运输最短路径分析系统的设计与实现

针对目前交通运输效力发挥不足的问题,研究道路网络模型构建和道路数据库设计,探讨分析交通运输最短路径分析流程,基于Dijkstra算法的基本原理,设计实现交通运输最短路径分析系统,从而优化运输资源配置,实现高质高效的交通运输。     

南方丘陵地区路域植被光谱处理与分析

以长沙—湘潭高速公路路域植被为研究对象,进行植被实地调查和野外光谱测定,分析主要植被类型光谱曲线,健康植被与非健康植被光谱差异,对植被光谱数据进行一阶微分和微分增量处理对比研究。研究表明:光谱反射率一阶微分和微分增量处理对消除土壤背景对地物光谱影响均有效,但微分增量处理更能突出植被信息。研究对遥感植被光谱分析具有一定参考价值。     

采用网络分区与预计算策略的k近邻查询算法

k近邻查询是GIS和空间数据库等领域的研究热点.文章针对道路网络中的k近邻查询,通过分区以及预计算近邻集策略,获得查询点的k近邻结果:首先利用自适应格网分区方法将道路网络划分为若干格网单元,并存储格网内节点与边界点、边界点之间距离;其次在网络中的节点预先存储近邻集.实验证明,在兴趣点密度高、k值较大情况下,算法具有较好的性能.     

基于复杂网络理论的城市道路网络自动综合方法

道路网络自动综合是地图综合的主要研究课题。本文通过引入对偶拓扑理论建立了城市道路网络的对偶拓扑结构,并将道路的重要性表达为路网中所有道路的重要度贡献的总和,进而提出了一种道路网络自动综合方法。实验表明,本文方法可以较为合理地选取路网中相对重要的道路,所选路网保持了原始路网的整体形态及拓扑连通。     

利用方向纹理特征从影像上搜索道路中心线

针对遥感影像道路中心线提取问题,提出利用方向纹理特征辅助道路中心线匹配搜索的思想,以人工给出的道路种子点和初始搜索方向为基础,运用方向纹理特征直接从遥感影像上匹配搜索道路的中心线。算法通过预测道路的宽度、弯曲等情况来自动调整纹理窗口的大小,因而能够很好地搜索到道路中心线,此外算法针对树木遮挡、车辆压盖设计了相应的处理策略。实验表明:该方法对遥感影像上不同类型道路均有很好的提取效果,且提取效率高于现有的道路提取算法。     

The role of the hyporheic zone across stream networks

Many hyporheic papers state that the hyporheic zone is a critical component of stream ecosystems, and many of these papers focus on the biogeochemical effects of the hyporheic zone on stream solute loads. However, efforts to show such relationships have proven elusive, prompting several questions: Are the effects of the hyporheic zone on stream ecosystems so highly variable in place and time (or among streams) that a consistent relationship should not be expected? Or, is the hyporheic zone less important in stream ecosystems than is commonly expected? These questions were examined using data from existing groundwater modelling studies of hyporheic exchange flow at five sites in a fifth‐order, mountainous stream network. The size of exchange flows, relative to stream discharge (Q_HEF:Q), was large only in very small streams at low discharge (area ≈ 100 ha; Q < 10 l/s). At higher flows (flow exceedance probability > 0·7) and in all larger streams, Q_HEF:Q was small. These data show that biogeochemical processes in the hyporheic zone of small streams can substantially influence the stream's solute load, but these processes become hydrologically constrained at high discharge or in larger streams and rivers. The hyporheic zone may influence stream ecosystems in many ways, however, not just through biogeochemical processes that alter stream solute loads. For example, the hyporheic zone represents a unique habitat for some organisms, with patterns and amounts of upwelling and downwelling water determining the underlying physiochemical environment of the hyporheic zone. Similarly, hyporheic exchange creates distinct patches of downwelling and upwelling. Upwelling environments are of special interest, because upwelling water has the potential to be thermally or chemically distinct from stream water. Consequently, micro‐environmental patches created by hyporheic exchange flows are likely to be important to biological and ecosystem processes, even if their impact on stream solute loads is small. Published in 2011 by John Wiley & Sons, Ltd.     

Comparison of bootstrap confidence intervals for an ANN model of a karstic aquifer response

Following many applications artificial neural networks (ANNs) have found in hydrology, a question has been rising for quantification of the output uncertainty. A pre‐optimized ANN simulated the hydraulic head change at two observation wells, having as input hydrological and meteorological parameters. In order to calculate confidence intervals (CI) for the ANN output two bootstrap methods were examined namely bootstrap percentile and BCa (Bias‐Corrected and accelerated). The actual coverage of the CI was compared to the theoretical coverage for different certainty levels as a means of examining the method's reliability. The results of this work support the idea that the bootstrap methods provide a simple tool for confidence interval computation of ANNs. Comparing the two methods, the percentile requires fewer calculations and yields narrower intervals with similar actual coverage to that of BCa. Overall, the actual coverage was proved lower than desired when not modeled points were present in the data subset. Copyright © 2011 John Wiley & Sons, Ltd.     

An ANN‐based model for spatiotemporal groundwater level forecasting

This paper evaluates the feasibility of using an artificial neural network (ANN) methodology for estimating the groundwater levels in some piezometers placed in an aquifer in north‐western Iran. This aquifer is multilayer and has a high groundwater level in urban areas. Spatiotemporal groundwater level simulation in a multilayer aquifer is regarded as difficult in hydrogeology due to the complexity of the different aquifer materials. In the present research the performance of different neural networks for groundwater level forecasting is examined in order to identify an optimal ANN architecture that can simulate the piezometers water levels. Six different types of network architectures and training algorithms are investigated and compared in terms of model prediction efficiency and accuracy. The results of different experiments show that accurate predictions can be achieved with a standard feedforward neural network trained usung the Levenberg–Marquardt algorithm. The structure and spatial regressions of the ANN parameters (weights and biases) are then used for spatiotemporal model presentation. The efficiency of the spatio‐temporal ANN (STANN) model is compared with two hybrid neural‐geostatistics (NG) and multivariate time series‐geostatistics (TSG) models. It is found in this study that the ANNs provide the most accurate predictions in comparison with the other models. Based on the nonlinear intrinsic ANN approach, the developed STANN model gives acceptable results for the Tabriz multilayer aquifer. Copyright © 2008 John Wiley & Sons, Ltd.     

广东近代诗歌海洋意识与海上丝路

广东近代诗歌浓郁的海洋意象与海上丝路有着密切的联系。海上丝路深刻地影响了广东近代文学 ,为之提供了丰富的创作题材和空间 ;而广东近代文学又反过来促进了海上丝路的发展和繁荣。