Bayesian area-to-point kriging using expert knowledge as informative priors

Area-to-point (ATP) kriging is a common geostatistical framework to address the problem of spatial disaggregation or downscaling from block support observations (BSO) to point support (PoS) predictions for continuous variables. This approach requires that the PoS variogram is known. Without PoS observations, the parameters of the PoS variogram cannot be deterministically estimated from BSO, and as a result, the PoS variogram parameters are uncertain. In this research, we used Bayesian ATP conditional simulation to estimate the PoS variogram parameters from expert knowledge and BSO, and quantify uncertainty of the PoS variogram parameters and disaggregation outcomes. We first clarified that the nugget parameter of the PoS variogram cannot be estimated from only BSO. Next, we used statistical expert elicitation techniques to elicit the PoS variogram parameters from expert knowledge. These were used as informative priors in a Bayesian inference of the PoS variogram from BSO and implemented using a Markov chain Monte Carlo algorithm. ATP conditional simulation was done to obtain stochastic simulations at point support. MODIS (Moderate Resolution Imaging Spectroradiometer) atmospheric temperature profile data were used in an illustrative example. The outcomes from the Bayesian ATP inference for the Matérn variogram model parameters confirmed that the posterior distribution of the nugget parameter was effectively the same as its prior distribution; for the other parameters, the uncertainty was substantially decreased when BSO were introduced to the Bayesian ATP estimator. This confirmed that expert knowledge brought new information to infer the nugget effect at PoS while BSO only brought new information to infer the other parameters. Bayesian ATP conditional simulations provided a satisfactory way to quantify parameters and model uncertainty propagation through spatial disaggregation.     

基于布尔矩阵的空间关联规则提取方法研究

空间关联规则是空间数据挖掘(SDM)中的重要内容之一。由于空间数据的复杂性,传统的空间关联规则挖掘方法主要是将空间数据库变换为非空间数据库,通过挖掘算法挖掘空间关联规则。目前,Apriori算法是关联规则挖掘中使用最为普遍的算法,但是,由于该算法在关联规则提取过程中需要多次扫描数据库,并且产生冗余的候选项集,因此,在执行大型数据库的关联规则挖掘时,具有效率低下的缺陷。本文基于Apriori算法提出了基于布尔矩阵的空间关联规则挖掘算法,并以挖掘福建省厦门市土地覆盖现状与地形特征因子的空间关联关系作为试验案例,对比Apriori算法的提取结果与提取效率,结果表明:该算法不仅减少了扫描数据库的次数,而且减少了冗余候选项集的产生,提高了空间关联规则的提取效率。     

基于引文网络的Cyberspace研究进展

以Web of Science数据库收录的有关研究Cyberspace的大量文献为数据源,采用文献计量学的分析方法,借助信息可视化软件CiteSpace Ⅱ对所采集的数据进行统计分析;获取1993 ～2013年检索数据,通过可视化图谱的方法挖掘出该领域的研究重点和学科分布;从地理学角度,揭示了该领域的研究热点和方向.     

胶北地区内生金矿成矿规律

胶北内生金矿是壳幔混源岩浆期后热液成矿 ,受复合基底EW向构造的早新华夏系和郭家岭超单元控制 ,蚀变、矿化分阶段连续进行 ,分为焦家式、灵山式、玲珑式及上庄式四个亚类型。金矿床区域上东西成行、南北成列、汇集成片呈棋盘格局 ;矿带内矿床沿走向等距分布、沿横向对应出现 ,矿床类型横向具分带、走向有变化、倾向相对稳定。矿体在控矿构造引张扩容段和构造叠加段赋存 ,并具斜立、侧伏、尖灭再现、斜列、瓦叠等产出规律。     

地图制图专家知识对地理信息符号化过程的控制

针对纸质地图数字化得到地理信息(空间数据)的过程中存在地图信息损失的问题,阐述了产生这种损失的原因。数字化后得到的地理信息是各种GIS应用系统的重要数据源,在GIS应用中需要把这些地理信息重新再现成地图。为了得到较好的地图显示效果,有必要将数字化后损失的地图信息在从地理数据到地图符号化的过程中进行补偿。论述了在符号化过程中如何充分利用地图制图专家的多年知识和经验,将用自然语言表达的不确定性的专家知识形式化,以此来控制整个符号化的过程,达到改善地图显示效果的目的。     

露天矿边坡实用型专家系统PESOPS V1.0的设计与应用

结合边坡工程和岩质边坡普遍存在的问题，建造了一个露天矿边坡实用型专家系统ＰＥＳＯＰＳ Ｖ１．０。介绍了滑坡预报子系统的原理和方法，通过应用实例表明该子系统适用性强，预报可靠、使用方便。     

湖北宜昌石板溪金矿区断层空间结构及其控矿规律研究

断层空间结构包括变形结构和几何结构两部分。本文在解析石板溪金矿区主要断层变形结构和几何结构的基础上，探讨了其空间结构模式和控矿规律。     

区域地质调查文本中文命名实体识别

作为我国地质调查领域最重要的数据源之一,地质调查报告中蕴含着丰富的地学知识及地质体描述等关键信息,准确高质量地抽取地质命名实体为地学知识图谱构建、知识推理及知识演化提供基础。笔者等在阐述地质命名实体识别任务基础上,分析地质实体不仅包含大量专业术语,还存在实体嵌套、大量长实体等领域特性,进一步增加了地质命名实体识别难度。笔者等提出一种基于轻量级预训练模型(ALBERT)—双向长短时记忆网络(BiLSTM)—条件随机场(CRF)模型的地质命名实体识别方法。首先利用ALBERT对输入字符上下文特征进行建模,并采用BiLSTM对其进行进一步上下文特征表征,最后采用CRF实现标注序列预测。实验结果表明,在构建的地质命名实体识别数据集上,相比于主流的命名实体识别模型算法,本文所提出的方法具有更好的抽取性能,提出的命名实体识别模型能为领域实体识别提供借鉴,同时为地学领域实体关系抽取和地学知识图谱构建提供有力方法支撑。     

Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan

ABSTRACT

“Panta Rhei – Everything Flows” is the science plan for the International Association of Hydrological Sciences scientific decade 2013–2023. It is founded on the need for improved understanding of the mutual, two-way interactions occurring at the interface of hydrology and society, and their role in influencing future hydrologic system change. It calls for strategic research effort focused on the delivery of coupled, socio-hydrologic models. In this paper we explore and synthesize opportunities and challenges that socio-hydrology presents for data-driven modelling. We highlight the potential for a new era of collaboration between data-driven and more physically-based modellers that should improve our ability to model and manage socio-hydrologic systems. Crucially, we approach data-driven, conceptual and physical modelling paradigms as being complementary rather than competing, positioning them along a continuum of modelling approaches that reflects the relative extent to which hypotheses and/or data are available to inform the model development process.

EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR not assigned     

Literature analysis of Dendrolimus disasters based on remote sensing monitoring

Due to global climate change, Dendrolimus pests and diseases seriously threaten the protection of forestry plants and the safety of crops all over the world. This paper aims to discuss the research results and frontier progress of Dendrolimus disasters based on remote sensing monitoring, trying to find the occurrence characteristics of pests. In this paper, bibliometric methods and CiteSpace knowledge graphs were used to analyze the publication trend, highly cited documents, key research institu...