中国道路网引起的景观破碎格局及其对保护区的影响

道路的修建往往带来景观破碎、生态系统功能受损等后果。有关道路网络的生态影响格局及其影响因素,是当前道路生态学、地理空间分析等领域共同关注的问题。本文利用2015年全国道路网络数据,采用定量空间分析方法,刻画了当前中国铺装道路所引起的陆表景观破碎的格局特征,并分析了道路网络对自然保护区的干扰及与多变量的影响关系。结果表明：① 全国铺装道路的生态影响面积占陆地面积的10%,将陆表切割成3万多个斑块,并呈现小斑块数目众多,大斑块数目较少的分布特点;同时,陆表破碎化程度呈现明显的东西分异规律,空间格局与人口分布、经济发展水平等影响因素的地理分布规律趋同;② 全国约58%的自然保护区受到道路干扰,保护区的受干扰情况存在高等级保护区受干扰弱、低等级保护区次之、国家公园受干扰程度最强等差异特征,且国家公园处于受干扰高于未设保护区域的状态;③ 主要人类活动因素与保护区受干扰程度呈正相关,保护区面积、地形因素与受干扰程度呈负相关,具有面积小、受保护等级低、位于平原地区、气候适宜等特征的保护区更易受到道路干扰,处于已经受到人类活动破坏,生态脆弱的状态。因此,中国道路建设应追求社会发展与生态保护双平衡,同时道路干扰受到自然以及人文要素的共同作用,在相关影响机制研究和生态保护政策制定中应进行综合考虑。     

A rational method for development of limit state for liquefaction evaluation based on shear wave velocity measurements

This papers presents a new approach for developing a limit state for liquefaction evaluation based on field performance data. As an example to illustrate the new approach, a database that consists of, among many other features, in situ shear wave velocity measurements and field observations of liquefaction/non‐liquefaction in historic earthquakes is analysed. This database is first used to train a neural network to classify liquefaction/non‐liquefaction based on soil resistance parameters and load parameters. The successfully trained and tested neural network is then used to establish a limit state, a multiple dimension boundary that separates ‘zone’ of liquefaction from ‘zone’ of non‐liquefaction. The limit state yields cyclic resistance ratio for a given set of soil resistance parameters. Examination of all cases in the database show that the developed limit state has a high degree of accuracy in predicting the occurrence of liquefaction/non‐liquefaction. The developed neural network model can accurately predict the cyclic resistance ratio of soils. Copyright © 2000 John Wiley & Sons, Ltd.     

GPS技术在金川露天矿边坡变形监测中的应用

本文结合金川露天矿边坡变形GPS监测研究,主要论述监测基点的选择,基点WGS-84坐标的确定,变形监测网的布设,GPS数据采集、处理和质量,坐标转换的实施等,给出反演推出的若干四等三角点及其它控制点的变形数据,这些数据和造成变形的物理成因符合得很好,最后概括出GPS技术应用于变形观测的若干优点。     

未确知测度BP神经网络模型在黑河流域水质预测中的应用

水质预测是水环境规划、评价和管理的重要依据,对促进水资源可持续利用及生态发展具有重要意义。针对水质预测中各项因子的不确定性,基于未确知测度理论(unascertained measure,UM),采用改变网络初值的方法,对BP神经网络加以改进,并利用黑河流域莺落峡水文站1998~2011年的水质监测资料进行分析和预测。以挥发酚为参考序列,用灰色关联方法分析参考序列与其他因子的关联度,并最终确定BP网络的输入节点为CODmn、DO、SO42-、Cr6+以及挥发酚,输出节点为挥发酚,从而建立UMBP模型。分析结果表明,UM-BP预测模型比标准的BP神经网络模型具有更高的预测精度。因此,该模型应用于黑河流域水质预测是可行的。     

基于BP神经网络的GPS导航算法

GPS导航解算中常用最小二乘算法。随着高动态用户需求精度的不断提高，且由于线性化忽略高次项，初始值精度低以及差分后剩余或放大误差的存在。导航解精度很难满足高动态用户的需求。为此，本文基于BP神经网络的非线性逼近性能。给出了基于BP神经网络的GPS导航算法。实测数据计算结果表明该算法能够真实地反映载体运动轨迹，其导航解的精度和可靠性有明显的提高。     

公交车辆全线运行状态的监督站牌及查询系统

针对出行者对公交车辆运行信息的需求,基于GPS技术设计了一个动态显示公交车辆位置信息的监督站牌系统,并提供公交位置信息的网络查询服务,方便出行者了解公交车辆的实时运营信息,以及对公交车辆的实时监督.系统以LabVIEW为软件开发平台对数据进行分析与处理,利用ASP.NET进行Web网页开发并与数据库交互,实现公交系统的智能运营和智能服务.通过实际验证,该方案具有可行性、实用性.     

Optimal design of rain gauge network in the Middle Yarra River catchment,Australia

Rainfall data are a fundamental input for effective planning, designing and operating of water resources projects. A well‐designed rain gauge network is capable of providing accurate estimates of necessary areal average and/or point rainfall estimates at any desired ungauged location in a catchment. Increasing network density with additional rain gauge stations has been the main underlying criterion in the past to reduce error and uncertainty in rainfall estimates. However, installing and operation of additional stations in a network involves large cost and manpower. Hence, the objective of this study is to design an optimal rain gauge network in the Middle Yarra River catchment in Victoria, Australia. The optimal positioning of additional stations as well as optimally relocating of existing redundant stations using the kriging‐based geostatistical approach was undertaken in this study. Reduction of kriging error was considered as an indicator for optimal spatial positioning of the stations. Daily rainfall records of 1997 (an El Niño year) and 2010 (a La Niña year) were used for the analysis. Ordinary kriging was applied for rainfall data interpolation to estimate the kriging error for the network. The results indicate that significant reduction in the kriging error can be achieved by the optimal spatial positioning of the additional as well as redundant stations. Thus, the obtained optimal rain gauge network is expected to be appropriate for providing high quality rainfall estimates over the catchment. The concept proposed in this study for optimal rain gauge network design through combined use of additional and redundant stations together is equally applicable to any other catchment. © 2014 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.     

Watershed structural influences on the distributions of stream network water and solute travel times under baseflow conditions

Watershed structure influences the timing, magnitude, and spatial location of water and solute entry to stream networks. In turn, stream reach transport velocities and stream network geometry (travel distances) further influence the timing of export from watersheds. Here, we examine how watershed and stream network organization can affect travel times of water from delivery to the stream network to arrival at the watershed outlet. We analysed watershed structure and network geometry and quantified the relationship between stream discharge and solute velocity across six study watersheds (11.4 to 62.8 km²) located in the Sawtooth Mountains of central Idaho, USA. Based on these analyses, we developed stream network travel time functions for each watershed. We found that watershed structure, stream network geometry, and the variable magnitude of inputs across the network can have a pronounced affect on water travel distances and velocities within a stream network. Accordingly, a sample taken at the watershed outlet is composed of water and solutes sourced from across the watershed that experienced a range of travel times in the stream network. We suggest that understanding and quantifying stream network travel time distributions are valuable for deconvolving signals observed at watershed outlets into their spatial and temporal sources, and separating terrestrial and in‐channel hydrological, biogeochemical, and ecological influences on in‐stream observations. Copyright © 2016 John Wiley & Sons, Ltd.     

Toward an improved global network for determination of tropospheric ozone climatology and trends

An examination of typical tropospheric ozone variability on daily, monthly, annual and interannual timescales and instrumental precision indicates that the current ozonesonde network is insufficient to detect a trend in tropospheric ozone of 1% per year at the 2 level even at stations with records a decade in length. From a trend prediction analysis we conclude that in order to detect a 1% per year trend in a decade or less it will be necessary to decrease the time between observations from its present value of 3–7 days to 1 day or less. The spatial distribution of the current ozonesonde stations is also inadequate for determining the global climatology of ozone. We present a quantitative theory taking into account photochemistry, surface deposition, and wind climatology to define the effectively sampled region for an observing station which, used in conjunction with the instrumental precision and the above prediction analysis, forms the basis for defining a suitable global network for determining regional and global ozone climatology and trends. At least a doubling of the present number of stations is necessary, and the oceans, most of Asia, Africa, and South America are areas where more stations are most needed. Differential absorption lidar ozone instruments have the potential for far more frequent measurements of ozone vertical profiles and hence potentially more accurate climatology and trend determinations than feasible with ozonesondes but may produce a (fair weather) biased data set above the cloud base. A strategy for cloudy regions in which either each station utilizes both lidars and sondes or each station is in fact a doublet comprised of a near-sea-level lidar and a proximal-mountain-top lidar could serve to minimize this bias.     

基于L-M神经网络的陡崖层状危岩失稳模式预测

明确危岩变形失稳模式是预防和治理研究的前提,但如今的传统预测方法存在成本高昂、实用性不强等缺点,尤其是在强烈地震诱发下。本文采用基于Levenberg-Marquardt法的神经网络结构,并借助三维离散元数值模拟手段,综合考虑了陡崖层状危岩的节理倾角、危岩边长、危岩高宽比、危岩堆积层数等影响变形失稳模式因素,以危岩变形失稳模式为研究对象,影响因素为切入点,建立了陡崖层状危岩变形失稳模式预测的神经网络模型。并基于由数值模拟计算得到的危岩变形失稳模式样本训练所建立的神经网络,最后分析了该预测模型的准确性。结果表明：该模型具有较好的学习和泛化能力,预测精度达到86.7%,验证了基于Levenberg-Marquardt法的神经网络预测危岩变形失稳模式的方法是有效且实际可行的。