Use of multivariate statistical methods in data-base maintenance

Multivariate statistical methods may be used to enhance large data bases by predicting missing information and verifying unchecked data vectors. In addition, new techniques may be used for exploratory purposes to investigate the interrelationships between variables. The techniques are being applied successfully in coal mining.     

REVIEWS OF GEOGRAPHIC SOFTWARE

Geographic Software Reviewed in this article: LIFE TABLES and the LESLIE MATRIX. CONDUIT MENTMAP2 . Lawrence W. Carstensen Jr . PC-MATLAB , Version 1.0. The Math Works, Inc. QUICKMAP , version 1.0. Sammamish Data Systems, Inc. SYSTAT: THE SYSTEM FOR STATISTICS , Version 2.1, Systat Inc. USA DISPLAY . Instant Tecall     

Application of a neural network model in establishing a stage–discharge relationship for a tidal river

This paper presents the applicability of neural network (NN) modelling for forecasting and filtering problems. The multilayer feedforward (MLFF) network was first constructed to forecast the tidal‐level variations at the mouth of the River Chao Phraya in Thailand. Unlike the well‐known conventional harmonic analysis, the NN model uses a set of previous data for learning and then forecasting directly the time‐series of tidal levels. It was found that lead time of 1 to 24 hourly tidal levels can be predicted successfully using only a short‐time hourly learning data. The MLFF network was further used to establish a stage–discharge relationship for the tidal river. The results show a considerably better performance of the NN model over the conventional models. In addition, the stage–discharge relationship obtained by the NN model can indicate reasonably well the important behaviour of the tidal influences. Copyright © 2003 John Wiley & Sons, Ltd.     

The key‐group method

This paper proposes an extension to the key‐block method, called ‘key‐group method’, that considers not only individual key blocks but also groups of collapsable blocks into an iterative and progressive analysis of the stability of discontinuous rock slopes. The basics of the key‐block method are recalled herein and then used to prove how key groups can be identified. We reveal that a key group must contain at least one basic key block, yet this condition is not entirely sufficient. The second block candidate for grouping must be another key block or a block whose movement‐preventing faces are common to one or more single key blocks. We also show that the proposed method yields more realistic results than the basic key‐block method and a comparison with results obtained using a distinct element analysis demonstrates the ability of this new method. Copyright © 2003 John Wiley & Sons, Ltd.     

On minimum spanning trees and the intergradation of clusters

Cluster analysis groups samples, but does not generally show gradations between clusters. To illustrate these relationships, principal-coordinate analysis and multidimensional scaling can be employed, but these methods may not be appropriate due to structural distortion. A minimum spanning tree (MST) computes a point-to-point path through the original matrix, and clusters are mapped on the MST diagram. The MST linkages, therefore, provide a natural continuum between the clusters, without distortion. Forty-six Silurian Tonoloway Limestone samples were analyzed for 16 sedimentary features indicative of subaerial exposure. Cluster analysis identified nine clusters which were reduced to five final, ordered groups by MST.     

Selection of environmental conditions for nearshore structure design

1　Introduction　InChina ,coastalcitiesaremostlyregionaleconomicdevelopmentcenters.Becauseoftheirspecialgeogra phiclocations ,typhoonsandassociatedstormsurgescauseheavylossesoflivesandproperties.In 1 992 Qing daowasfloodedduetothecoincidenceoftheastrono m…     

Reconstruction of Three-Dimensional Pacific Temperature with Argo and Satellite Observations

Combining Argo temperature and salinity profiles with satellite-derived sea surface temperature (SST), weekly three-dimensional temperature fields of the Pacific Ocean were reconstructed for the period January 2000 to December 2010. The data reconstruction methodology we used has three salient features: the first is a combined application of the Optimal Interpolation (OI) and the Multivariate Data Interpolating Empirical Orthogonal Function (MDINEOF) methods, the second is an emphasis on the multivariate nature of the vertical thermohaline structure, and the third is the inclusion of a continuous satellite-derived SST dataset to compensate for the discontinuities and the lack of surface observations in Argo data. Comparisons with other observations and data products indicate that our gridded temperature product is able to capture the main features of temperature distribution as well as its variability from intraseasonal to interannual time scales, thus providing a useful new dataset for ocean and climate studies.

RÉSUMÉ?[Traduit par la rédaction] En combinant les profils de température et de salinité Argo avec les températures de la surface de la mer dérivées des données satellitaires, nous avons reconstruit les champs tridimensionnels hebdomadaires de température de l'océan Pacifique pour la période de janvier 2000 à décembre 2010. La méthodologie de reconstruction des données que nous avons utilisée présente trois points saillants : le premier est une application combinée des méthodes de l'interpolation optimale et de la fonction orthogonale empirique d'interpolation de données multivariées (MDINEOF), le deuxième est une mise en évidence de la nature multivariée de la structure thermohaline verticale et le troisième est l'inclusion d'un ensemble continu de données de température de la surface de la mer dérivées des données satellitaires pour compenser les discontinuités et le manque d'observations de surface dans les données Argo. Les comparaisons avec d'autres observations et produits de données montrent que notre produit de températures à des points de grille est capable de discerner les caractéristiques principales de la distribution des températures de même que sa variabilité de l’échelle intrasaisonnière à l’échelle interannuelle, ce qui fournit un nouvel ensemble de données utile pour les études océaniques et climatiques.     

一种确定联合反演中相对权比的两步法

提出确定联合反演中相对权比的两步法。第1步是不考虑相对权比,建立两类或多类数据的联合反演模型,通过赫尔墨特方差分量估计使得两类或多类观测数据的单位权方差相等（或单位权方差的比值接近1）,从而理论上确定观测数据合理的权阵,同时对不同种类数据进行归一化;第2步是在获得合理的观测数据权阵之后,建立顾及相对权比的联合反演模型,...     

热带印度洋-太平洋热力异常联合模对我国夏季降水的影响

基于我国160个台站的降水资料和西太平洋副热带高压指数,利用典型相关分析和合成分析等方法,研究了热带印度洋-太平洋热力异常联合模对我国夏季降水的影响,并探讨了该联合模与西太平洋副热带高压的关系。结果表明,该联合模对我国夏季降水有一年到半年的超前影响,且以冬季联合模对次年夏季降水的影响最显著。当冬季联合模出现正异常时,次年夏季我国四川地区、长江流域、华北南部降水偏多,而华北北部和江南大部分地区降水偏少;反之亦然。进一步分析表明,冬季热带印度洋-太平洋热力异常联合模对我国夏季降水的影响可能是通过夏季西太平洋副热带高压的变动来实现的。     

舟山近海日本囊对虾(Marsupenaeus japonicus)野生群体与露天养殖越冬群体间的形质差异

鉴于舟山沿海日本囊对虾秋苗养殖成活率显著低于春季和夏季人工养殖苗种之实际,深入开展适合于本地沿海养殖的日本囊对虾秋苗的优质亲体的选种研究显得十分必要。为此,作者采用聚类分析、主成分分析和判别分析等方法定量研究了舟山近海日本囊对虾野生群体和露天池塘越冬养成群体间的形质特征差异。结果表明:两者在本研究所涉18项形质评价指标中的相似性指数仅为11.11%,欧氏距离为4.243(P0.01);经主成分分析,提取到的5个特征值均大于1的主成分,累计贡献率达85.27%,其中第1主成分为以营养生理状况指数、额剑上缘锯齿数以及腹部肌肉发达程度为主导的体质强壮因子,第2主成分为以寻食视野与胸腔空间大小为主导的捕食运动功能因子,后3个主成分为以尾节肌肉发达程度为主导的避敌运动功能因子;采用逐步判别法,以判别贡献率较大的9项形质评价指标为自变量,所建Fisher分类函数方程组可清晰区分日本囊对虾野生群体和越冬养成群体,其中野生群体的判别准确率P1、P2分别为99.33%和96.13%,越冬养成群体的判别准确率P1、P2分别为96%和99.31%,两者综合判别准确率为97.67%。因此,日本囊对虾秋苗野生亲本形质特征的选择,应以秋苗越冬养成群体为参照。