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Introduction Data mining (SHAO and YU, 2003) is a new kind of technique developed with database and artificial intelligence in recent years, which processes the data in the database to abstract the im- plied and pre-unknown, but potentially useful information and knowledge from large amounts of incomplete, noisy, blurring and stochastic data. For data mining, data purging is an important link beforehand that includes eliminating noise, making up lost domain, and deleting ineffective data, as… 相似文献
383.
WU Yongfu & XU Jiyao Laboratory for Space Weather Center for Space Science Applied Research Chinese Academy of Sciences Beijing China 《中国科学D辑(英文版)》2005,48(10)
Gravity waves play a significant role in establishing the large-scale circulation and structure of the middle atmosphere. Through gravity wave saturation proc-esses, such motions are believed to cause turbulence, resulting in divergence of momentum flux and the diffusion of heat and constituents in the meso-sphere[1,2]. The mechanisms that contribute signifi-cantly to the gravity wave saturation are thought to be the dynamical and convective instabilities[3]. However, it is difficult to distin… 相似文献
384.
XU Jianming XU Xiangde LIU Yu DING Guoan CHEN Huailiang HU Jiangkai ZHANG Jianchun WU Hao LI Weiliang HE Jinhai YANG Yuanqin WANG Jiahe 《中国科学D辑(英文版)》2005,48(Z2)
Based on analysis of the air pollution observational data at 8 observation sites in Beijing including outer suburbs during the period from September 2004 to March 2005, this paper reveals synchronal and in-phase characteristics in the spatial and temporal variation of air pollutants on a city-proper scale at deferent sites; describes seasonal differences of the pollutant emission influence between the heating and non-heating periods, also significantly local differences of the pollutant emission influence between the urban district and outer suburbs, i.e. the spatial and temporal distribution of air pollutant is closely related with that of the pollutant emission intensity. This study shows that due to complexity of the spatial and temporal distribution of pollution emission sources, the new generation Community Multi-scale Air Quality (CMAQ) model developed by the EPA of USA produced forecasts, as other models did, with a systematic error of significantly lower than observations, albeit the model has better capability than previous models had in predicting the spatial distribution and variation tendency of multi-sort pollutants. The reason might be that the CMAQ adopts average amount of pollutant emission inventory, so that the model is difficult to objectively and finely describe the distribution and variation of pollution emission sources intensity on different spatial and temporal scales in the areas, in which the pollution is to be forecast. In order to correct the systematic prediction error resulting from the average pollutant emission inventory in CMAQ, this study proposes a new way of combining dynamics and statistics and establishes a statistically correcting model CMAQ-MOS for forecasts of regional air quality by utilizing the relationship of CMAQ outputs with corresponding observations, and tests the forecast capability. The investigation of experiments presents that CMAQ-MOS reduces the systematic errors of CMAQ because of the uncertainty of pollution emission inventory and improves the forecast level of air quality. Also this work employed a way of combining point and area forecasting, i.e. taking the products of CMAQ for a center site to forecast air pollution for other sites in vicinity with the scheme of model products "reanalysis" and average over the "area". 相似文献
385.
The chemical forms, spatial distribution and sources of As, Hg, Cd, Pb and Zn in sediments of the Miyun reservoir were studied. The results of sequential extraction demonstrate that most of As, Pb and Zn were bound to the residual fraction, Hg was associated with the sulfide fraction while Cd was associated with the carbonate fraction and the residual fraction. On the vertical profiles the concentrations of the heavy metals in total and each fractions mostly decreased with increasing depths in sediments, suggesting that the heavy metals input from the upstream watershed increases yearly. Summation of the residual fraction, the sulfide fraction and the carbonate fraction accounts for 60.03%―85.60% of the total heavy metal contents in the sediments, which represent the geochemical background values of the elements and relate closely to soil erosion. Results of the main factor analysis show that most sediments of the reservoir come from the upstream soil erosion, the point source pollution and domestic waste. Moreover, the microbial activities taking place on the sediment-water interface are also one of the major factors to cause the increasing content of the organic matter fraction and the iron-manganese oxide fraction. Environmental change of the reservoir water could make the removability of the heavy metals increase, leading to the increase of their concentrations in pore water in sediments, and imperiling water quality of the reservoir. 相似文献
386.
针对水文测站与上级机关距离较远的特点,设计采用目前发展迅速的Internet网络技术,实现对水文测站测验的远程计算机监视和遥控操作控制。文中介绍了系统的工作原理及软硬件设计方法,系统的建成为上级领导防汛决策提供了及时、可靠的信息和依据,对推进水文现代化建设具有较大作用。 相似文献
387.
The aim of this paper is to investigate the feasibility of using Landsat TM data to retrieve leaf area index (LAI). To get a LAI retrieval model based ground reflectance and vegetation index, detailed field data were collected in the study area of eastern China, dominated by bamboo, tea plant and greengage. Plant canopy reflectance of Landsat TM wavelength bands has been inversed using software of 6S. LAI is an important ecological parameter. In this paper, atmospheric corrected Landsat TM imagery was utilized to calculate different vegetation indices (VI), such as simple ratio vegetation index (SR), shortwave infrared modified simple ratio (MSR), and normalized difference vegetation index (NDVI). Data of 53 samples of LAI were measured by LAI-2000 (LI-COR) in the study area. LAI was modeled based on different reflectances of bands and different vegetation indices from Landsat TM and LAI samples data. There are certainly correlations between LAI and the reflectance of Tm3, TM4, TM5 and TM7. The best model through analyzing the results is LAI = 1.2097*MSR + 0.4741 using the method of regression analysis. The result shows that the correlation coefficient R2 is 0.5157, and average accuracy is 85.75%. However, whether the model of this paper is suitable for application in subtropics needs to be verified in the future. 相似文献
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