The spectroscopic orbit of the K‐giant binary γ Canis Minoris

We have determined an improved orbit for the bright, evolved, double lined binary γ Canis Minoris. The system has an orbital period of 389.31 days and an eccentricity of 0.2586. We have revised the secondary to primary mass ratio to 0.987. The spectral types of the primary and secondary are K4 III and K1: III, respectively, and the components have a V magnitude difference of 2.2. Orbital fits to the Hipparcos astrometry are not definitive, but they suggest an orbital inclination of ∼ 66°, which produces masses of 1.88 and 1.85 M_⊙ for the components. A comparison with evolutionary tracks results in an age of 1.3 Gyr. STELLA very low amplitude radial velocity residuals of the secondary indicate a period of 278 days. We interpret this as the rotation period of the secondary, detectable because of star spots rotating in and out of view. This period is nearly identical to the pseudosynchronous rotation period of the star. The primary is rotating more slowly than its pseudosynchronous rate. (© 2013 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

冀东地区冷口断裂重磁异常特征及其地质作用

利用冀东地区1∶2．5万高精度航空磁测资料及收集的地面重力资料,阐述了冷口断裂及其两侧航磁、重力异常特征,并结合物性特征,分析了航磁、重力异常的地质成因及其对地质构造和矿产的控制作用。冷口断裂两侧航磁、重力异常特征差别十分明显,反映了两侧结晶基底、盖层和侵入岩分布规律差异较大,认为该断裂不仅控制了地质构造分布,也间接控制了矿产分布,可为地学研究和矿产勘查提供必要的地球物理依据。     

A comparative study of three neural network forecast combination methods for simulated river flows of different rainfall—runoff models

Abstract

The performances of three artificial neural network (NN) methods for combining simulated river flows, based on three different neural network structures, are compared. These network structures are: the simple neural network (SNN), the radial basis function neural network (RBFNN) and the multi-layer perceptron neural network (MLPNN). Daily data of eight catchments, located in different parts of the world, and having different hydrological and climatic conditions, are used to enable comparisons of the performances of these three methods to be made. In the case of each catchment, each neural network combination method synchronously uses the simulated river flows of four rainfall—runoff models operating in design non-updating mode to produce the combined river flows. Two of these four models are black-box, the other two being conceptual models. The results of the study show that the performances of all three combination methods are, on average, better than that of the best individual rainfall—runoff model utilized in the combination, i.e. that the combination concept works. In terms of the Nash-Sutcliffe model efficiency index, the MLPNN combination method generally performs better than the other two combination methods tested. For most of the catchments, the differences in the efficiency index values of the SNN and the RBFNN combination methods are not significant but, on average, the SNN form performs marginally better than the more complex RBFNN alternative. Based on the results obtained for the three NN combination methods, the use of the multi-layer perceptron neural network (MLPNN) is recommended as the appropriate NN form for use in the context of combining simulated river flows.     

Multi-decadal variations in annual maximum peak flows in semi-arid and temperate regions of Chile

Abstract

In determining the possible influence of climate change, it is important to understand the temporal and spatial variability in streamflow response for diverse climate zones. Thus, the aim of this study was to determine the presence of changes in annual maximum peak flow for two climate zones in Chile over the past few decades. A general analysis, a flood frequency analysis and a trend analysis were used to study such changes between 1975 and 2008 for a semi-arid (29°S–32°S) and a temperate (36°S–38°S) climatic zone. The historic annual maxima, minima and mean flows, as well as decadal mean peak flow, were compared over the period of record. The Gumbel distribution was selected to compare the 30-year flood values of two ±15-year intervals, which showed that streamflow decreased by an average of 19.5% in the semi-arid stations and increased by an average of 22.6% in the temperate stations. The Mann-Kendall test was used to investigate the temporal changes in streamflows, with negative trends being observed in 87% of the stations analysed in the semi-arid zone, and positive trends in 57% of those analysed in the temperate zone. These differences in streamflow response between climate zones could be related to recent documented increases in altitude of the zero-degree isotherm in the Andes Mountains of Chile, since most of the significant positive and negative changes were detected in first-order rivers located closer to this mountain range.

Editor D. Koutsoyiannis; Associate editor H. Lins

Citation Pizarro, R., Vera, M., Valdés, R., Helwig, B., and Olivares, C., 2013. Multi-decadal variations in annual maximum peak flows in semi-arid and temperate regions of Chile. Hydrological Sciences Journal, 59 (2), 300–311.     

Municipal water demand forecasting under peculiar fluctuations in population: a case study of Mashhad,a tourist city

ABSTRACT

Forecasting future water demands has always been of great complexity, especially in the case of tourist cities which are subject to population fluctuations. In addition to the usual uncertainties related to climate and weather variables, daily water consumption in Mashhad, a tourist city is affected by a significant different fluctuation. Mashhad is the second most populous city in Iran. The number of tourists visiting the city is subject to national and religious events, which are respectively based on the Iranian formal calendar (secular calendar) and the Arabic Hijri calendar (Islamic religious calendar). Since religious events move relative to the secular calendar, the coincidence of the two calendars results in peculiar wild fluctuations in population. Artificial neural networks (ANNs) are chosen to predict water demand under such conditions. Three types of ANNs, feedforward back-propagation, cascade-forward and radial basis functions, are developed. In order to track how population fluctuation propagates in the model and affects the outputs, two sets of inputs are considered. For the first set, based on evaluating several repetitions, a typical combination of variables is selected as inputs, whereas for the second set, new calendar-based variables are included to decrease the effect of population fluctuations; the results are then compared using some performance criteria. A large number of runs are also conducted to assess the impact of random initialization of the weights and biases of networks and also the effect of calendar-based inputs on improvement of network performance. It is shown that, from the points of view of performance measures and unchanging outputs through numerous runs, the radial basis network that is trained by patterns including calendar-based inputs can provide the best domestic water demand forecasting under population fluctuations.

Editor D. Koutsoyiannis Associate editor E. Rozos     

基于模糊隶属函数的参数稳健估计

参数估计过程经常遇到2个主要问题:一个是最小二乘与稳健估计不能兼顾最优无偏性和稳健性;另一个是非线性模型参数估计进行线性近似处理中带来的模型误差导致对粗差的错误鉴别和定位。针对以上2个问题,提出了基于模糊隶属函数的稳健估计方法。该方法通过隶属度加权来削弱个别粗差污染数据对参数估计结果的影响,从而达到提高参数估计稳健性的目的。分别用线性回归模型和非线性回归模型对该算法进行了验证,结果表明,该算法对粗差具有较好的抵抗能力,能够对参数进行稳健估计。     

坐标换算程序设计及实现

我国常用的坐标系有如下几种:1954年北京坐标系、1980年国家大地坐标系、WGS-84世界大地坐标系、2000国家大地坐标系。本文对上述四种坐标系进行了深入研究,重点研究在同一参考基准下的坐标换算,涉及的内容有空间直角坐标同大地坐标相互换算、高斯坐标正反算以及邻带换算。本文以Visual Basic 6.0为编程语言,研制了相应的坐标换算软件,现有的资料处理结果表明程序设计是合理的,软件可行性较好。     

GSQL的设计与实现

目前,简单的关系数据库与面向对象模型都不能满足用户对数据的管理应用要求,因此,面向对象的关系数据库应运而生。同时为了对空间数据库进行高效的查询、管理,产生了空间查询语言(GSQL)。本文主要进行了空间查询语言的初步探索,详细地阐述了在OpenGIS矢量数据模型上的GSQL语言的扩展过程,其中包括在Oracle中创建空间数据类型、空间函数的方法,并进行了详细的举例说明。     

基于辐射状靶标的天绘一号卫星CCD相机分辨率在轨检测

分辨率是表示摄影成像系统和评价影像质量的重要指标,准确把握成像系统的分辨率在轨变化情况,关系到遥感影像的合理有效利用。本文采用地面铺设辐射状靶标的方法,利用天绘一号卫星获取的靶标影像,对搭载的CCD相机分辨率进行在轨检测,计算了成像系统沿轨和垂轨方向的分辨率,简要分析了检测结果的合理性。     

Analysis of radial basis function interpolation approach

The radial basis function （RBF） interpolation approach proposed by Freedman is used to solve inverse problems encountered in well-logging and other petrophysical issues. The approach is to predict petrophysical properties in the laboratory on the basis of physical rock datasets, which include the formation factor, viscosity, permeability, and molecular composition. However, this approach does not consider the effect of spatial distribution of the calibration data on the interpolation result. This study proposes a new RBF interpolation approach based on the Freedman＇s RBF interpolation approach, by which the unit basis functions are uniformly populated in the space domain. The inverse results of the two approaches are comparatively analyzed by using our datasets. We determine that although the interpolation effects of the two approaches are equivalent, the new approach is more flexible and beneficial for reducing the number of basis functions when the database is large, resulting in simplification of the interpolation function expression. However, the predicted results of the central data are not sufficiently satisfied when the data clusters are far apart.