基于贝叶斯网络的生态系统服务空间格局优化——以泾河流域为例

城市高速发展引起了区域土地利用格局的改变,不仅影响生态环境质量,而且还对生态系统服务的空间格局产生影响,在此基础上优化生态系统服务显得至关重要。在泾河流域2000—2020年净初级生产力（Net primary productivity, NPP）、农业生产力、土壤保持和产水服务空间评估的基础上,将贝叶斯网络和生态系统服务相结合,在关键变量子集和可视化的最优状态子集的基础上,评估了4种生态系统服务需要优化的区域,为区域经济和生态和谐发展提供参考。结果表明：（1） 水文评价模型（Soil and water assessment tool, SWAT）模型能较准确地模拟区域的径流量。通过模拟值和观测值对比分析,该模型具有较高的决定性系数（R²>0.6）和纳什效率系数（NSE>0.5）,可为进一步评估产水服务提供保障。（2） 2000—2020年泾河流域4种生态系统服务的时空差异性较为显著。在时间尺度上,4种生态系统服务均呈现波动中上升的趋势,在空间尺度上呈现较为稳定的变化趋势。（3） 通过对4种生态系统服务优化区域进行叠加分析,发现综合优化区域集中在彭阳县的中部和西南部以及环县的零星区域。研究结果对指导优化区域进行生态系统的可持续管理以及改善生态系统的退化状况具有重要意义。     

信息物理系统研究与应用综述

信息物理系统是以信息世界与物理世界的紧密结合为显著特征的,集计算、通信与控制为一体的网络系统。首先介绍信息物理系统的起源与发展。比较分析几个比较有代表性的信息物理系统定义,给出对信息物理系统的概念的理解与定义。在文献梳理的基础上,总结信息物理系统的主要特征。简要介绍3个典型的信息物理系统应用方案,即智能电网、生物与医疗、智能高速公路与无人驾驶。最后总结信息物理系统研究与应用中所面临的挑战,提出今后信息物理系统可能的研究热点。为更好地了解信息物理系统相关知识,为信息物理系统领域的研究提供有价值的参考。     

郑州市市区风环境模拟研究

城市风环境是城市微气候研究的一个重要方向,对分析城市热岛效应、空气流通等具有重要意义。本文以郑州市市区为例,使用1971—2018年气象观测数据、2018年建筑分布数据（OSM）和2016年资源三号卫星数据作为数据源,通过运用气象学和GIS技术结合的方法,探究潜在通风廊道,科学量化城市形态对风环境的影响。研究首先借助WindNinja软件,对城市背景风环境进行模拟分析,该计算方法提高了风道定位的精度。然后利用卫星遥感数据制作了数字高程模型（DSM）,结合OSM计算下垫面地表粗糙度。进一步借助ArcGIS软件,利用最小成本路径法（LCP）确定城市潜在通风廊道的位置。结果表明：① 郑州市近年来平均风速缓慢下降,平均每10年下降0.26 m/s;全年主导风向东北风进入城市后受城市形态影响在京广铁路线附近以西逐渐转为东北偏东风,其中在京广快速路以东风速较高,在京广快速路以西风速较低;② 金水区西部、中原区、二七区以及管城区的地表粗糙度较高,通风环境较差;金水区东部和惠济区的地表粗糙度较低,通风环境较好;③根据盛行风向模拟的潜在通风廊道,其共同特点是趋向于低粗糙度的地区。     

Explorations into the formal structure of drainage basins

Every basin of higher than first order is drained by a channel network composed of two subnetworks. Their basins are separated by a drainage divide line, called the basin divider, which is the primary organizing feature of the main basin. Each basin of magnitude n contains n – 1 subnetworks of higher order, and is therefore organized by a set of n – 1 dividers. The dividers and the basin boundary are interconnected in a graph called the divider network of the basin; in graph-theoretic terms this network forms a tree and has the same magnitude and link numbers as the channel network draining the basin. While the subbasins and subnetworks of a drainage basin form a nesting hierarchy, the corresponding dividers do not; indeed, any two dividers share at most one node in common, and whether they do so is independent of whether the corresponding subbasins are nesting or disjoint. However, the dividers of nesting basins are linked by recursive relationships which permit the derivation of a set of algebraic equations; these equations relate the dividers of a basin to other basin components; for example, their combined length is equal to half the length of all first-order basin boundaries minus the length of the main basin boundary. The second part of the paper explores the dependence of the divider length on other basin parameters. The expected length, as predicted by the assumption of topological randomness, is clearly rejected by the data. An alternative approach (regression) is based on the observed magnitudes of the subbasins separated by each divider, and is reasonably successful in estimating divider length. The last section introduces the concept of the standardized basin defined by a boundary length of unity; the estimated lengths of the basin divider and the basin boundary permit an approximate reconstruction of the idealized basin shape and the location of the divider in it.     

Supervised neural network recognition of habitat zones of rice invertebrates

This study aimed to evaluate effectiveness and performance of several supervised neural network models and make pattern recognition on invertebrate habitat zones. Probabilistic, general regression, and linear neural networks, and discriminant analysis were used to recognize both known and unknown invertebrate habitat zones. The results showed that neural network models were better than traditional discriminant analysis in the recognition of known habitat zones. There was not distinctive variation in recognition from different neural network models. Sensitivity analysis indicated that the learning rate of the neural network would influence recognized results. An unknown invertebrate species from Lepidoptera was recognized to be soil-dweller (dryland) by both neural network models and discriminant analysis. In sensitivity analysis it was additionally recognized to be the type of plant canopy (terrestrial). Overall the species was estimated to be a soil-dweller (dryland) or live on plant canopy (terrestrial). It was concluded that neural network models can perform better than conventional statistic models in pattern recognition, but a comprehensive comparison among various models is necessary in order to achieve a high reliable recognition and prediction. Furthermore, sensitivity analysis can lead to an in-depth grasp on the mechanism in the recognition and is thus needed.     

Topological properties of disjoint channel networks within enclosed regions

Enclosure of some portion of one or more natural stream-drainage basins by superposition of a rectangle on a map of drainage network results in fragmentation of the natural basins into a set of disjoint channel networks. Each of these may have some channel links and forks of the natural network plus truncated links intersected by the enclosure boundary. The topological properties of the network elements in the enclosure are used to set up a model of random network patterns, in which the number of disjoint channel networks is expressed as a function of the number of links and forks in the enclosures. This function is shown to be a multiplicative constant times the square root of the number of links or forks. Empirical data on square and rectangular enclosures of several sizes from the Inez (Kentucky)Quadrangle map showed that the predicted multiplicative constants do not agree with observation, but that the square-root relation seems to hold at least to a first approximation. The models thus can be used as a norm against which deviations of real-world enclosures from network pattern randomness can be studied.     

现代综合业务数字网的现状与展望

现代通信网正在从公共电话交换网向综合数字网、综合业务数字网、宽带综合业务数字网发展，以适应电信现代化建设的需要。     

深基坑工程变形预报神经网络法的初步研究

提出了深基坑变形预报的人工神经网络法,详细介绍了该方法的建模和应用实例。预报结果与实测值较为吻合,从而表现在深基坑工程中利用该方法进行变形预报是可行的。     

Automatic classification of dome instabilities based on Doppler radar measurements at Merapi volcano, Indonesia: Part I

Monitoring lava dome instabilities is crucial to efficiently monitor active dome building volcanoes. The Doppler radar technique provides a unique opportunity to gather information about the number of instability events occurring at the growing dome and about the dynamic processes that take place during different types of instabilities. So far, three different kinds of processes have been identified: sliding material, gravitational break-offs and explosive outbursts. In addition, Doppler radars provide rain measurements, which can be used to investigate possible correlations between rainfall and dome activity. Two radar systems have been installed at Merapi volcano in October 2001 and January 2005 to continuously monitor dome instabilities. Due to the large number of instability events that occur during times of high activity, manual processing and analysis of instability events is not practical for monitoring purposes. Therefore, an automatic classification system has been developed, which is capable of identifying different kinds of instabilities as well as rainfall. Two different kinds of classifier models have been applied: (1) neural network and (2) K-nearest-neighbour classifier model. Both classify Doppler spectra according to the underlying dynamic process, that is, rain, sliding material, gravitational break-off or explosive outburst. The classifiers are able to identify disturbances, which have no physical source, but are merely artefacts from the radar device itself. Because radar events are sequences of Doppler spectra, a rule set has been defined, which finally determines the event class. All classifiers have been trained and tested on independent data sets to estimate the classification performance. The overall classification rate is about 90 per cent. Discrimination of instabilities and non-volcanic events reaches about 98 per cent accuracy.     

顾及邻域居民地群组相似性的道路网匹配方法

现有多源同比例尺道路网匹配方法中,大多只利用道路自身特征进行匹配,而较少顾及道路周边要素对匹配过程的影响和约束,从而影响了道路网匹配效果的进一步提高,特别是对系统误差改正后仍存在一定位置或旋转偏差的道路数据进行匹配时,这种影响尤为明显。本文借鉴人类对陌生环境的空间认知特点,提出了一种顾及邻域居民地群组相似性的道路网匹配方法。该方法通过构建城市骨架线网确定与道路相邻的居民地群组,进而计算居民地群组空间关系和几何特征相似度来获得对应道路的匹配结果。其特点在于:对存在位置或旋转偏差的道路数据匹配,以其邻域空间内居民地群组的整体相似性指标来带动道路自身匹配,实际上是增加了周边居民地群组对道路匹配过程的约束,更具鲁棒性。试验及对比分析表明,本方法能够较好地解决系统误差改正后仍存在较大位置和旋转偏差的道路数据间的匹配问题,提高匹配的正确率。