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1.
蒸散发是水圈、大气圈和生物圈中水分循环和能量交换的纽带。在全球尺度上,蒸散发约占陆地降水总量的60%;作为其能量表达形式,潜热通量约占地表净辐射的80%。随着通量观测技术的发展,全球长期持续的观测数据得以获取和共享,近年来基于数据驱动的蒸散发遥感反演方法取得了较好的研究进展。本文针对数据驱动的蒸散发遥感反演方法和产品,从经验回归、机器学习和数据融合3个方面展开,对现有的研究进展进行了梳理、归纳和总结,并从驱动数据、反演方法、已有产品等方面指出目前仍存在的问题和不足。未来仍需开展数据驱动的高时空分辨率的蒸散发遥感反演方法的研究,有效考虑地表温度和土壤水分等可以指示地表蒸散发短期变化的重要信息,同时加强基于过程驱动的物理模型与数据驱动的模型的结合,使两类模型能互为补充、各自发挥所长,共同推动蒸散发遥感反演研究水平的进步。  相似文献   
2.
化石数据是了解地球历史以及深时生命演化的重要信息来源。通过数百年的积累,古生物学家已经发表了海量的古生物学数据。过去三、四十年里,随着计算机、数据库和互联网技术的快速发展,国内外涌现出大量的古生物学数据库,彼此间的目标、体系架构、数据组织方式和服务对象通常存在显著差异,呈现百花齐放的特点。文章系统介绍了古生物学领域主要数据库的发展历史、数据表结构、数据特征和数据量等建设情况,对比分析了其数据整理方式、核心在线功能、数据共享特点和数据质量控制措施。同时,结合近年来数据驱动下的古生物学领域的科学研究实例,提出一站式全生态链数据平台的建设设想,为深时数字地球(DDE)建设多学科融合、数据开放与共享的大数据平台提供参考。  相似文献   
3.
基于反馈模型的地表相关多次波消除方法SRME(Surface-Related Multiple Elimination)近年来已得到了广泛的应用.利用共聚焦点CFP(Common Focus Point)道集代替炮集,可以将该方法扩展至层间多次波的消除.地表数据驱动的层间多次波消除方法直接利用地表观测数据进行层间多次波预测,避免了构建CFP道集所需的聚焦运算,特别是与层相关的层间多次波消除方法有效提高了多次波预测的计算效率.但地表数据驱动的与层相关的层间多次波消除方法并没有从理论上被严格地推导证明,其与CFP方法之间的关系亦未被讨论.本文在CFP方法的理论基础上推导了地表数据驱动的与层相关的层间多次波消除方法,阐明了CFP方法与地表数据驱动方法之间的内在联系.并将该方法应用于模型数据和野外实际数据,应用实例表明了所提方法的有效性.  相似文献   
4.
将一种基于数据驱动的RegEM算法引入GPS坐标时间序列插值中,分别采用模拟不同比例连续缺失数据与实测含缺失数据,比较RegEM与拉格朗日方法、三次样条方法、正交多项式方法的插值效果与性能。结果表明,对于模拟不同比例连续缺失数据,RegEM算法插值效果均优于传统方法,且在大量数据连续缺失的情况下效果最优;对于实测含缺失数据,RegEM方法插值所得序列保留方差最大化效果最好,约为正交多项式方法的1.17倍、三次样条方法的1.38倍。  相似文献   
5.
The spatial distribution of discovered resources may not fully mimic the distribution of all such resources, discovered and undiscovered, because the process of discovery is biased by accessibility factors (e.g., outcrops, roads, and lakes) and by exploration criteria. In data-driven predictive models, the use of training sites (resource occurrences) biased by exploration criteria and accessibility does not necessarily translate to a biased predictive map. However, problems occur when evidence layers correlate with these same exploration factors. These biases then can produce a data-driven model that predicts known occurrences well, but poorly predicts undiscovered resources. Statistical assessment of correlation between evidence layers and map-based exploration factors is difficult because it is difficult to quantify the “degree of exploration.” However, if such a degree-of-exploration map can be produced, the benefits can be enormous. Not only does it become possible to assess this correlation, but it becomes possible to predict undiscovered, instead of discovered, resources. Using geothermal systems in Nevada, USA, as an example, a degree-of-exploration model is created, which then is resolved into purely explored and unexplored equivalents, each occurring within coextensive study areas. A weights-of-evidence (WofE) model is built first without regard to the degree of exploration, and then a revised WofE model is calculated for the “explored fraction” only. Differences in the weights between the two models provide a correlation measure between the evidence and the degree of exploration. The data used to build the geothermal evidence layers are perceived to be independent of degree of exploration. Nevertheless, the evidence layers correlate with exploration because exploration has preferred the same favorable areas identified by the evidence patterns. In this circumstance, however, the weights for the “explored” WofE model minimize this bias. Using these revised weights, posterior probability is extrapolated into unexplored areas to estimate undiscovered deposits.  相似文献   
6.
In this paper, we describe new fuzzy models for predictive mineral potential mapping: (1) a knowledge-driven fuzzy model that uses a logistic membership function for deriving fuzzy membership values of input evidential maps and (2) a data-driven model, which uses a piecewise linear function based on quantified spatial associations between a set of evidential evidence features and a set of known mineral deposits for deriving fuzzy membership values of input evidential maps. We also describe a graphical defuzzification procedure for the interpretation of output fuzzy favorability maps. The models are demonstrated for mapping base metal deposit potential in an area in the south-central part of the Aravalli metallogenic province in the state of Rajasthan, western India. The data-driven and knowledge-driven models described in this paper predict potentially mineralized zones, which occupy less than 10% of the study area and contain at least 83% of the model and validation base metal deposits. A cross-validation of the favorability map derived from using one of the models with the favorability map derived from using the other model indicates a remarkable similarity in their results. Both models therefore are useful for predicting favorable zones to guide further exploration work.  相似文献   
7.
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  相似文献   
8.
ABSTRACT

An appropriate streamflow forecasting method is a prerequisite for implementation of efficient water resources management in the water-limited, arid regions that occupy much of Iran. In the current research, monthly streamflow forecasting was combined with three data-driven methods based on large input datasets involving 11 precipitation stations, a natural streamflow, and four climate indices through a long period. The major challenges of rainfall–runoff modelling are generally attributed to complex interacting processes, the large number of variables, and strong nonlinearity. The sensitivity of data-driven methods to the dimension of input/output datasets would be another challenge, so large datasets should be compressed into independently standardized principal components. In this study, three pre-processing techniques were applied: singular value decomposition (SVD) provided more efficient forecasts in comparison to principal component analysis (PCA) and average values of inputs in all networks. Among the data-driven methods, the multi-layer perceptron (MLP) with 1-month lag-time outperformed radial basis and fuzzy-based networks. In general, an increase in monthly lag-time of streamflow forecasting resulted in a decline in forecasting accuracy. The results reveal that SVD was highly effective in pre-processing of data-driven evaluations.  相似文献   
9.
风暴潮是指由强烈的大气扰动所导致的海面异常升高现象,由热带气旋引起的风暴潮常对沿海地区造成巨大的社会经济、人类活动和生命财产危害。依靠数据驱动的强非线性映射能力的机器学习方法较传统数值模式预报在耗费研究资源和计算时间上更具优势。本文选取广东省珠江口为研究区域,基于卷积长短时记忆网络(Convolutional LSTM network,ConvLSTM)机器学习算法展开风暴潮漫滩预报研究,利用由再分析资料驱动的数值模式产品构建了历史台风漫滩数据集,用于机器学习模型训练、验证和测试。研究了两种预报方式,一种是基于海表面高度场的自回归预报,另一种是依赖预报风场和初始海表面高度场进行的预报;它们可以实现基于数据驱动的风暴潮漫滩预报,其中自回归预报模型表现更优。相较于传统动力学数值预报,基于数据驱动的ConvLSTM预报模型结构更为轻便,所需驱动数据更少,在缺少边界条件、地形、径流等信号时,在短临预报中仍能基本复现数值模式模拟的结果。  相似文献   
10.
轮式机器人执行巡逻、播种和工业生产等任务是一个强非线性的间歇过程.针对重复运行的轮式机器人轨迹跟踪问题,本文提出了一种基于数据驱动的高阶迭代学习控制算法.首先,对轮式移动机器人的模型进行推导设计,并对推导得到的状态空间形式的离散时间模型利用基于状态转移的迭代动态线性化方法,将轮式机器人系统转化为线性输入输出数据模型;其次,设计高阶迭代优化目标函数得到控制律,并利用参数更新律估计线性输入输出数据模型中的未知参数.控制器的设计和分析只使用系统的输入输出数据,不包含任何显式的模型信息.通过采用高阶学习控制方法,在控制律中利用更多之前迭代的控制输入信息,提高了控制性能.最后,仿真结果验证了该方法在轮式机器人轨迹跟踪控制中的有效性.  相似文献   
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