Criteria of geographic relevance: an experimental study

The relevance of geographic information has become an emerging problem in geographic information science due to an enormous increase in volumes of data at high spatial, temporal, and semantic resolution, because of ever faster rates of new data capturing. At the same time, it is not clear whether the concept of relevance developed in information science and implemented for document-based information retrieval can be directly applied to this new, highly dynamic setting. In this study, we analyze the criteria users apply when judging the relevance of geographic entities in a given mobile usage context. Two different experiments have been set up in order to gather users' opinions on a set of possible criteria, and their relevance judgements in a given scenario. The importance ascribed to the criteria in both experiments clearly implies that a new concept of relevance is required when dealing with geographic entities instead of digital documents. This new concept of ‘Geographic Relevance’ is highly dependent on personal mobility and user's activity, whose understanding may in turn be refined by the assimilation of ‘Geographic Relevance’ itself.     

Comparison of direct statistical and indirect statistical-deterministic frameworks in downscaling river low-flow indices

ABSTRACT

This work explores the ability of two methodologies in downscaling hydrological indices characterizing the low flow regime of three salmon rivers in Eastern Canada: Moisie, Romaine and Ouelle. The selected indices describe four aspects of the low flow regime of these rivers: amplitude, frequency, variability and timing. The first methodology (direct downscaling) ascertains a direct link between large-scale atmospheric variables (the predictors) and low flow indices (the predictands). The second (indirect downscaling) involves downscaling precipitation and air temperature (local climate variables) that are introduced into a hydrological model to simulate flows. Synthetic flow time series are subsequently used to calculate the low flow indices. The statistical models used for downscaling low flow hydrological indices and local climate variables are: Sparse Bayesian Learning and Multiple Linear Regression. The results showed that direct downscaling using Sparse Bayesian Learning surpassed the other approaches with respect to goodness of fit and generalization ability.

Editor D. Koutsoyiannis; Associate editor K. Hamed     

Assessing relevance of tweets for risk communication

ABSTRACT

Although Twitter is used for emergency management activities, the relevance of tweets during a hazard event is still open to debate. In this study, six different computational (i.e. Natural Language Processing) and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event. Primarily, tweets containing information about the flooding events and its impacts were analysed. Examination of the relationships between tweet volume and its content with precipitation amount, damage extent, and official reports revealed that relevant tweets provided information about the event and its impacts rather than any other risk information that public expects to receive via alert messages. However, only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event. By providing insight into the quality of social media data and its usefulness to emergency management activities, this study contributes to the literature on quality of big data. Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness.     

岩土压力传感器匹配误差的特性

在介绍了岩土压力传感器匹配误差概念的基础上,论述了匹配误差的稳定性、等效性、真实性和实用性以及各特性之间的相关性。通过研究得出：匹配误差特性的核心是稳定性;传感器刚性饼状设计是保证稳定性的必要条件;等效匹配误差不一定是真实的,但即使是不真实,也不影响实用性。     

江苏省海洋经济与区域经济耦合协调性研究

伴随着海洋资源开发和海洋科技进步,海洋产业在国民经济中扮演着日益重要的角色,海洋经济与区域经济的协调发展成为研究热点。江苏省是沿海地区经济发展强省,但其海洋经济发展优势并不突出,为了对其海洋经济与区域经济的耦合协调发展状态及耦合发展的制约因素进行研究,文章在探析海洋经济系统与区域经济系统耦合发展机理的基础上,借鉴物理学耦合度函数构建了海洋经济与区域经济的耦合协调度模型,并对2006~2012年江苏及其他沿海省市海洋经济与区域经济的耦合协调度进行了评价。结果表明2006~2012年间,江苏省海洋经济与区域经济的耦合协调度不断提高,逐步迈入了磨合发展的关键期,但海洋产业发展模式落后和海陆互动性弱等因素导致其与沿海其他省市相比仍有较大差距。本研究能够为江苏海洋经济发展提供科学建议。     

河流与城镇体系结构形成的关联特征及空间表现

基于相互作用视角分析了河流与城镇体系结构形成的关联特征及空间表现。河流作为重要自然要素,与城镇体系结构的形成演变存在紧密的关联性,并在空间上表现出不同的特征,研究结果表明:1从数量结构来看,城镇依河流呈非均衡集中分布格局;2规模结构方面,城镇对河流依赖随规模递增而增强,且大城市分布与河流等级、区位密切相关;3职能结构方面,沿河城镇职能具有河流属性特征,且区域中心城市或综合性城市多沿河分布;4空间结构方面,河流引导沿岸城镇空间布局分异,改变中心地空间布局理论结构,且引导沿岸城镇空间体系呈现出点—轴、双核等结构模式。     

硇洲岛大型海藻群落的季节演替*

2011年4月至2012年1月对硇洲岛潮间带大型海藻进行了周年的季节调查, 结果表明, 调查海域大型海藻共有64种。其中褐藻门15种, 占总种类数的23.44%; 红藻门28种, 占总种类数的43.75%; 绿藻门20种, 占总种类数的31.25%; 蓝藻门1种, 占总种类数的1.56%。其种类数春季最多, 共43种; 夏季24种; 秋季29种; 冬季31种。有9个物种为4个季节共有种, 有14个物种为3个季节共有种。各季节间共有种类数为12~26种, 季节间种类更替率为0.42~0.78, 春夏季种类更替率最高, 秋冬季种类更替率最低。优势种共有13种, 仅拟鸡毛菜Pterocladiella capillacea为全年优势种, 而半叶马尾藻Sargassum hemiphyllum和小珊瑚藻Corallina pilulifera为3个季节共有优势种。调查海域大型海藻生物量季节变化明显, 平均生物量春季最高, 冬季次之, 夏季最低。不同物种其垂直分带明显, 从高潮区往低潮区种类数不断增多; 生物量也是从高潮区往低潮区逐渐增大。物种多样性指数变化范围为0.03~2.33, 年均值为1.10; 均匀度变化范围为0.01~0.70, 年均值为0.36; 种类丰富度指数变化范围为0.15~1.65, 年均值为0.72; 辛普森优势度指数变化范围为0.01~0.78, 年均值为0.39。各大型海藻含水率变化范围在51.92%~97.52%, 平均值为85.21%; 总有机碳含量变化范围在4.34%~42.06%, 平均为27.99%。相关性分析发现, 调查海域大型海藻生物量与无机氮(DIN)呈显著负相关, 相关系数为0.49(P＜0.05), 与其他环境因子的相关性不明显。在大型海藻生长旺盛的冬春季, 海水中的无机氮(DIN)含量最低, 与其他自然海域冬季营养盐积累规律显著不同。     

水库滑坡成因机制研究进展与展望

滑坡是水库库区主要地质灾害类型之一,开展水库滑坡成因机制研究具有重要理论意义和工程应用价值.利用WebofScience(WoS)数据库和VOSviewer文献计量工具对1999-2018年已发表的969篇以水库滑坡为主题的相关论文进行研究趋势分析.文献计量分析表明三峡库区滑坡稳定性和变形研究是未来水库滑坡成因机制研究主要趋势.从库水对滑坡的宏观力学作用方式、库水作用下岩土体渗流应力耦合机理和库水对岩土体劣化作用过程等方面,对国内外水库滑坡成因机制研究的主要成果与进展进行了综述.综合现有的研究成果指出水库滑坡在精细化地质建模、岩土体多场耦合特征参数获取和岸坡长期演化评价等方面尚存在不足.基于上述问题,提出水库滑坡成因机制研究应以多场信息监测为重要手段,立足多学科交叉,采用大数据融合与挖掘和人工智能技术等解决水库滑坡长期演化趋势难题.考虑水库滑坡所处地质环境的复杂性,建议未来应在水库滑坡立体精细地质建模、多场关联监测、地质结构多场多尺度演变过程、基于监测数据大数据分析的滑坡预警阈值确定和原位试验综合平台构建等方面进一步深入研究.     

Relevance vector machine applied to slope stability analysis

This paper examines the potential of relevance vector machine (RVM) in slope stability analysis. The nonlinear relationship between slope stability and its influence factors is presented by the relevance vector learning mechanism based on a kernel‐based Bayesian framework. The six input variables used for the RVM for the prediction of stability slope are density (γ), friction angle (C), friction coefficient (?), slope angle (?_r), slope height (H), and pore water pressure (r_u). Comparison of RVM with some other methods is also presented. RVM has been used to compute the error bar. The results presented in this paper clearly highlight that the RVM is a robust tool for the prediction of slope stability. The experimental results show the effectiveness of the proposed approach. Copyright © 2011 John Wiley & Sons, Ltd.     

基于主成分回归的阿克苏市城市需水量预测

科学地预测城市需水量对城市的发展具有十分重要的意义。城市需水量受到多重因素的影响,各因素之间的相关性较大,从而导致一些预测计算结果失真,使一些公式的适用性不是很强。为此,采用主成分回归建模,借助主成分分析与典型相关分析的思路,有效地解决了各因子之间的相关问题,建立了城市需水量预报模型。结果表明．模型的拟合和预报精度均较好。