Coastal risk adaptation: the potential role of accessible geospatial Big Data

Increasing numbers of people are living in and using coastal areas. Combined with the presence of pervasive coastal threats, such as flooding and erosion, this is having widespread impacts on coastal populations, infrastructure and ecosystems. For the right adaptive strategies to be adopted, and planning decisions to be made, rigorous evaluation of the available options is required. This evaluation hinges on the availability and use of suitable datasets. For knowledge to be derived from coastal datasets, such data needs to be combined and analysed in an effective manner. This paper reviews a wide range of literature relating to data-driven approaches to coastal risk evaluation, revealing how limitations have been imposed on many of these methods, due to restrictions in computing power and access to data. The rapidly emerging field of ‘Big Data’ can help overcome many of these hurdles. ‘Big Data’ involves powerful computer infrastructures, enabling storage, processing and real-time analysis of large volumes and varieties of data, in a fast and reliable manner. Through consideration of examples of how ‘Big Data’ technologies are being applied to fields related to coastal risk, it becomes apparent that geospatial Big Data solutions hold clear potential to improve the process of risk based decision making on the coast. ‘Big Data’ does not provide a stand-alone solution to the issues and gaps outlined in this paper, yet these technological methods hold the potential to optimise data-driven approaches, enabling robust risk profiles to be generated for coastal regions.     

Effect of Arching on Uplift Capacity of Single Piles

An analytical method has been proposed to predict the ultimate uplift capacity of single vertical piles embedded in sand considering arching effect. The present analysis takes into consideration of various pile and soil parameters such as length (L), diameter (d) of the pile, angle of internal friction of soil (ϕ), soil pile friction angle (δ) and unit weight of soil (γ). A modified value of coefficient of lateral earth pressure in uplift has been developed considering the arching effect of soil. A comparative assessment of the uplift capacity of piles predicted by using proposed theory and the existing available theories is made with the existing field and model test results. It has been observed that the present model considering the arching effect predicts the results closer.     

论"大三峡旅游经济圈"构建中的鄂西与渝东区域整合

该文论述构建“大三峡旅游经济团”的关键是实现鄂西与渝东的区域整合。鄂西与渝东在地缘和旅游开发上关系密切,但隶属不同的省级行政区,在新的形势下很有必要进行一体化开发。同时阐述区域整合和旅游一体化开发的意义,提出构建“大三峡旅游经济圈”的若干对策。     

The 'Grasses' of the Big Scrub District,North-eastern New South Wales: Their recent history,spatial distribution and origins

A feature of the pre-European landscape of the sub-tropical Richmond River district of north-eastern New South Wales was a large expanse of rainforest known as the Big Scrub. In and around the Big Scrub were small patches of grassland and grassy open-forest, known locally as 'grasses'. These were often given individual names, which indicated their importance in the early timber-based economy of this generally grassless district for camping and depasturing working stock. Historical records enable a reconstruction of the distribution of 56 named 'grasses', and also allow some inferences to be made about their botany and ecology. The 'grasses' appear to be natural features of the landscape, mainly relict areas following invasion of the late Pleistocene open-forest vegetation by rainforest, following sea level rise, during the Holocene. A toponymic study of the use of the term 'grass' in the Richmond River district is also included.     

温棚樱桃生产丰歉年的气候分析

通过对大棚内外大樱桃萌芽到成熟期间空气温度和湿度的观测 ,结合其物候期 ,分析了温湿度与樱桃生长发育及产量的关系。结果表明 ,棚内温度条件明显优越于露地 ,但温棚内温湿度调控是否得当是影响大棚樱桃产量的关键。     

World Heritage in danger: Big data and remote sensing can help protect sites in conflict zones

World Heritage sites provide a key mechanism for protecting areas of universal importance. However, fifty-four UNESCO sites are currently listed as “In Danger”, with 40% of these located in the Middle East. Since 2010 alone, thirty new sites were identified as under risk globally. We combined big-data and remote sensing to examine whether they can effectively be used to identify danger to World Heritage in near real-time. We found that armed-conflicts substantially threaten both natural- and cultural-heritage listed sites. Other major risks include poor management and development (globally), poaching (Africa mostly) and deforestation (tropics), yet conflict is the most prominent threat. We show that news-mining of big-data on conflicts and remote sensing of nights-lights enabled us to identify conflict afflicted areas in near real-time. These findings provide a crucial avenue for developing a global transparent early-warning system before irreversible damage to world heritage takes place.     

大数据与数学地球科学研究进展——大数据与数学地球科学专题代序

大数据与数学地球科学的核心应用技术包括高维数据降维、图像数据处理、无限数据流挖掘、机器学习、关联规则算法与推荐系统算法等。人工智能地质学,包括大数据-智能矿床成因模型与找矿模型的构建,是具有重要价值的研究方向。高维数据降维旨在从初始高维特征集合中选出低维特征集合,有效地消除无关和冗余特征,增强学习结果的易理解性。哈希算法、聚类分析、主成分分析等是较常用的数学降维工具。机器学习是人工智能的核心,是使计算机具有智能的根本途径。机器学习与人工智能各种基础问题的统一性观点正在形成。深度学习的训练模型往往需要海量数据作为支撑,因此迁移学习方法日益受到重视。图像模式识别是大数据挖掘的重要技术。网络中的社区结构识别对理解整个网络的结构和功能有重要价值,可帮助分析、预测网络各元素间的交互关系。沉浸式虚拟现实技术是实现大数据可视化的重要方向,对具有多元、异构、时空性、非线性、多尺度地质矿产勘查数据的展示要求有特别的价值。引入VR技术进行矿产地质大数据的可视化,可实现大数据时代矿产勘查数据的新认知。无限数据流在地质、地球化学、地球物理监测中大量存在,甚至可以持续自动产生。对数据流数据的计算包括对点查询、范围查询、内积查询、分位数计算、频繁项计算等。关联规则和推荐系统算法是大数据挖掘中的重要算法,其应用范围越来越广泛。贝叶斯原理在大数据时代有独特的价值,贝叶斯网络是成因建模的一个革命性工具。智能地质学研究刚刚起步,构建大数据-智能矿床成因模型与找矿模型是智能地质学研究的重要内容。矿床模型研究方式的变革,将出现于互联网、云计算技术环境下全球各地的矿床研究团队的共同参与。     

论地质研究中的因果关系和相关关系——大数据研究的启示

我们对大数据两年多来的研究表明,大数据是一个非常有效的方法。一直以来,人们都是通过因果关系来认识世界的;而大数据不是,大数据是从数据出发,挖掘数据之间的相关关系,从而提升数据的价值。例如在矿床学研究中,人们往往过分关注矿床的成因,关注成矿与岩浆、流体与岩体之间的因果关系。实际上,流体与岩体、矿床与岩浆之间主要不是因果关系而是相关关系。早先的玄武岩构造环境判别图几乎都是按照因果关系的思路设计的,虽然取得了一定的效果但并不完美。我们采用大数据方法对全球全体玄武岩和安山岩的数据进行挖掘,取得了极佳的效果,主要依据的是元素之间的相关关系而非因果关系。多少年来,人们在科学研究的实践活动中习惯于对因果关系的追求。现在,科学的发展要求我们更加重视数据之间的相关关系,从对因果关系的追求转变为对相关关系的追求。实践表明,追求因果关系不可避免人为因素的干扰,而大数据方法挖掘数据之间的相关关系,在很大程度上避免了人为因素的干扰,因此,大数据的结果是真实可靠的。     

10～30d延伸期天气预报方法研究进展与展望

10~30 d延伸期预报是国际大气科学关注的研究热点。这一时间段的预报对开展防灾、救灾工作具有极其重要的价值和意义,该工作需要结合初始气象条件、海洋、大气以及气候的影响因素,其中观测资料具有复杂性、综合性、全球性等,这些科学大数据反映和表征着复杂的自然现象与关系,具有高度数据相关性和多重数据属性,预测过程十分复杂。分析了延伸期预报的各种主流方法,其中重点介绍了动力模式、经典统计和大数据方法 3类预报方法的研究现状,并探讨了各种方法的优势和不足,在此基础上对目前延伸期预报领域存在的问题进行了讨论和总结。对延伸期预报方法的未来发展方向和应用前景给以展望。