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251.
252.
Vertical turbulent fluxes of water vapour, carbon dioxide, and sensible heat were measured from 16 August to the 28 September
2006 near the city centre of Münster in north-west Germany. In comparison to results of measurements above homogeneous ecosystem
sites, the CO2 fluxes above the urban investigation area showed more peaks and higher variances during the course of a day, probably caused
by traffic and other varying, anthropogenic sources. The main goal of this study is the introduction and establishment of
a new gap filling procedure using radial basis function (RBF) neural networks, which is also applicable under complex environmental
conditions. We applied adapted RBF neural networks within a combined modular expert system of neural networks as an innovative
approach to fill data gaps in micrometeorological flux time series. We found that RBF networks are superior to multi-layer
perceptron (MLP) neural networks in the reproduction of the highly variable turbulent fluxes. In addition, we enhanced the
methodology in the field of quality assessment for eddy covariance data. An RBF neural network mapping system was used to
identify conditions of a turbulence regime that allows reliable quantification of turbulent fluxes through finding an acceptable
minimum of the friction velocity. For the data analysed in this study, the minimum acceptable friction velocity was found
to be 0.15 m s−1. The obtained CO2 fluxes, measured on a tower at 65 m a.g.l., reached average values of 12 μmol m−2 s−1 and fell to nighttime minimum values of 3 μmol m −2 s−1. Mean daily CO2 emissions of 21 g CO2 m−2d −1 were obtained during our 6-week experiment. Hence, the city centre of Münster appeared to be a significant source of CO2. The half-hourly average values of water vapour fluxes ranged between 0.062 and 0.989 mmol m−2 s−1and showed lower variances than the simultaneously measured fluxes of CO2. 相似文献
253.
The objective of this study is to improve the statistical modeling for the ternary forecast of heavy snowfall in the Honam area in Korea. The ternary forecast of heavy snowfall consists of one of three values, 0 for less than 50 mm, 1 for an advisory (50–150 mm), and 2 for a warning (more than 150 mm). For our study, the observed daily snow amounts and the numerical model outputs for 45 synoptic factors at 17 stations in the Honam area during 5 years (2001 to 2005) are used as observations and potential pre... 相似文献
254.
基于MATLAB的主成分RBF神经网络降水预报模型 总被引:13,自引:3,他引:10
以前期500 hPa高度场、海温场为预报因子,采用径向基函数(RBF)神经网络与主成分分析相结合的方法,建立了广西中部5月平均降水预报模型。在5年独立样本的预测检验中,预测的平均相对误差、均方误差及平均绝对误差分别为18.12%、50.52和34.23。对比分析RBF神经网络与BP(Back Propagation)神经网络的预测结果,表明RBF神经网络预测结果更准确、精度更高。 相似文献
255.
遥感成像过程中,地面、大气等诸多要素的不确定性和波段之间的相关性等原因影响了分类精度,导致变化检测的不准确性。为了提高分类精度往往需要引入先验知识。贝叶斯网络是一种新的数据表达和推理模型,对数据没有严格的正态分布前提要求,通过动态地调整先验概率密度,能有效提高分类精度。以北京通州地区1996-05-29和2001-05-19两个时相的陆地卫星Landsat TM遥感影像为例,介绍了基于贝叶斯网络的分类算法,并在此基础上实现了两个时相遥感影像的变化检测。实验结果表明:基于贝叶斯网络分类算法的后分类比较变化检测方法是遥感影像变化检测的一种新的有效方法。 相似文献
256.
无线传感器网络中基于时钟漂移补偿的时间同步技术 总被引:4,自引:0,他引:4
在分析和总结现有时间同步算法的特点及其存在问题的基础上,提出了一种在无线传感器网络的时间同步协议(TPSN)中同时考虑节点时钟偏移和频率漂移率的改进算法。该算法利用连续两次同步过程中所记录的时间信息来估算节点时钟的偏移和频率漂移率,并进行补偿。同传统算法相比,此算法通讯成本较低、精度较高。最后通过数学分析和仿真的方法对所提出的改进算法进行了验证。 相似文献
257.
Hung-Ming Lin Shun-Kung Chang Jian-Hong Wu C. Hsein Juang 《Engineering Geology》2009,104(3-4):280-289
This paper is aimed at creating an empirical model for assessing failure potential of highway slopes, with a special attention to the failure characteristics of the highway slopes in the Alishan, Taiwan area prior to, and post, the 1999 Chi-Chi, Taiwan earthquake. The basis of the study is a large database of 955 slope records from four highways in the Alishan area. Artificial neural network (ANN) is utilized to “learn” from this database. The developed ANN model is then used to study the effect of the Chi-Chi earthquake on the slope failure characteristics in the Alishan area. Significant changes in the degrees of influence of several factors (variables) are found and possible reasons for such changes are discussed. The novelty of this paper lies in the fact that the developed ANN models are used as a tool to investigate the slope failure characteristics before and after the Chi-Chi earthquake. 相似文献
258.
Prediction of pile settlement using artificial neural networks based on standard penetration test data 总被引:4,自引:0,他引:4
F. Pooya Nejad Mark B. Jaksa M. Kakhi Bryan A. McCabe 《Computers and Geotechnics》2009,36(7):1125-1133
In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions. 相似文献
259.
北京环线建设驱动的土地利用变化遥感检测与分析 总被引:6,自引:0,他引:6
近年来,北京市的环线建设大大改进了北京的交通状况,促进了环线周边的商业发展和房地产开发的繁荣,同时也带来了环线周边地区的土地利用的变更。在“科技奥运”科技攻关项目和在研自然科学基金项目的支撑下,选择了1988,1994,2001和2003年5—6月份的TM数据,在影像经过几何校正和辐射归一化校正后对其应用高精度的自组织神经网络分类和利用高分辨率的航空照片验证;在分类数据和变更信息的基础上,采用了空间自相关分析模型、频率指数模型和潜力模型分别对北京各种用地类型的频率指数和建设用地的Moran系数和Geary系数,以及城市增长的潜力指她进行计算。一系列计算显示了环线驱动的北京市土地利用变化情况。结果表明:在环线建设的驱动下,北京市的建设用地围绕环线呈快速增长趋势;绿地面积在四环、五环的环线一带也呈增加趋势;城市增长潜力从四环到六环呈逐渐增大趋势。 相似文献
260.