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Quantitative analyses of groundwater flow and transport typically rely on a physically‐based model, which is inherently subject to error. Errors in model structure, parameter and data lead to both random and systematic error even in the output of a calibrated model. We develop complementary data‐driven models (DDMs) to reduce the predictive error of physically‐based groundwater models. Two machine learning techniques, the instance‐based weighting and support vector regression, are used to build the DDMs. This approach is illustrated using two real‐world case studies of the Republican River Compact Administration model and the Spokane Valley‐Rathdrum Prairie model. The two groundwater models have different hydrogeologic settings, parameterization, and calibration methods. In the first case study, cluster analysis is introduced for data preprocessing to make the DDMs more robust and computationally efficient. The DDMs reduce the root‐mean‐square error (RMSE) of the temporal, spatial, and spatiotemporal prediction of piezometric head of the groundwater model by 82%, 60%, and 48%, respectively. In the second case study, the DDMs reduce the RMSE of the temporal prediction of piezometric head of the groundwater model by 77%. It is further demonstrated that the effectiveness of the DDMs depends on the existence and extent of the structure in the error of the physically‐based model. 相似文献
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针对当前时空数据管理中存在的主要问题,本文提出用PostgreSQL作为平台来进行时空数据的管理。本文从PostgreSQL自身的特点出发,并结合“香港18个地区区议会选举”的例子,分析、探讨了如何在PostgreSQL的环境中实现对时空数据的有效管理。并在此基础上,对时空数据的合理组织进行了研究,为时空数据的管理提供了新的思路。 相似文献
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We present H monochromatic and spectroscopic observations of the sudden disappearance of a dark filament located near the center of the solar disk on October 26, 1989. The event was not associated with the flare activity. The dark filament first disintegrated into two loop-like components, and then each component successively showed ascending motion with a velocity greater than 30 km s–1. Comparison of the H pictures taken before and after the start of this event suggests that the dark filament was originally composed of two magnetic flux loops. 相似文献
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In the southwest of China, one of the greatest threats to local ecosystem is the area expansion of an invasive species, i.e.,
Eupatorium adenophorum Spreng (EAS). In this study, the remote-sensing technology was used to detect and map the spatial distribution
of EAS in Guizhou Province, China. A series of vegetation indices, including normalized difference vegetation index (NDVI),
simple ratio index (SRI) and atmospherically resistant vegetation index (ARVI), were used to identify EAS from HJ-A Chninese
satellite data. According to the analysis results of fieldworks from March 21 to 22, 2009, it was found that the vegetation
index of {1.9589 ≤ SRI ≤ 4.1095}∩{0.2359 ≤ ARVI ≤ 0.5193} was the optimal remote-sensing parameter for identifying EAS from
HJ-A data. According to the spatial distribution of EAS estimated from HJ-A data, it was found that EAS was rather more in
southwest of Guizhou Province than in northeast. EAS became sparse from southwest to northeast gradually, and the central
Guizhou Province was the ecological corridor linking EAS in southwest to that in northeast. By comparison with validated data
collected by the government of Guizhou Province, it was found that the uncertainty of remote-sensing method was 18.52%, 29.31%,
8.77% and 9.46% in grassland, forest, farmland and others respectively, and the mean uncertainty was 13.29%. Owing to the
lower height of EAS than many plants in forest, the uncertainty of EAS was the greatest in forest than that in grassland,
farmland and so on. 相似文献
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Biological soil crusts (BSCs) are ubiquitous communities of diminutive organisms such as cyanobacteria, green algae, lichens, mosses and others associated closely with particles of surface soil, forming a cohesive thin horizontal layer. The ecological roles of BSCs affecting soil nutrient cycling, stability and hydrological processes, influencing the germination and establishment of vascular plants, and serving as habitats for numerous arthropods and microorganism have been well documented. We tested the hypothesis that micro‐geomorphological features determine the spatial distribution of BSCs by reallocating related abiotic resources at small‐ and medium‐scales in the Tengger Desert. Our results showed that higher soil pH and higher total potassium content in topsoil positively correlated with the colonization of cyanobacteria and algae in the earliest successional stages of BSCs, while increasing dust deposition onto the topsoil enhanced the development of lichen and mosses in the later stages of BSCs. Increasing soil moisture raised the proportion of mosses and lichen in BSCs, this will possibly change the ecological functions of BSCs, such as nitrogen‐fixation by cyanobacteria, due to the conversion from a complex to relative simple type of BSC. Micro‐geomorphology has created various habitats at a small‐scale affecting colonization and development of cryptogams. This paper considers the contribution of micro‐geomorphology to biodiversity in the extreme arid desert systems. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
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