Effect of spatial variation characteristics on contouring of design storm depth

The ordinary kriging method, a geostatistical interpolation technique, was applied for developing contour maps of design storm depth in northern Taiwan using intensity–duration–frequency (IDF) data. Results of variogram modelling on design storm depths indicate that the design storms can be categorized into two distinct storm types: (i) storms of short duration and high spatial variation and (ii) storms of long duration and less spatial variation. For storms of the first category, the influence range of rainfall depth decreases when the recurrence interval increases, owing to the increasing degree of their spatial independence. However, for storms of the second category, the influence range of rainfall depth does not change significantly and has an average of approximately 72 km. For very extreme events, such as events of short duration and long recurrence interval, we do not recommend usage of the established design storm contours, because most of the interstation distances exceed the influence ranges. Our study concludes that the influence range of the design storm depth is dependent on the design duration and recurrence interval and is a key factor in developing design storm contours. Copyright © 2003 John Wiley & Sons, Ltd.     

Identification of physical processes inherent in artificial neural network rainfall runoff models

The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.     

Principal components in active galactic nuclei variability data and the estimation of the flux contributions from different components

It has been found that the near-infrared flux variations of Seyfert galaxies satisfy relations of the form   F_i ≈α _{i j} +β _{i j} F_j   , where F_i , F_j are the fluxes in filters i and j ; and  α _{i , j} , β _{i , j}   are constants. These relations have been used to estimate the constant contributions of the non-variable underlying galaxies. The paper attempts a formal treatment of the estimation procedure, allowing for the possible presence of a third component, namely non-variable hot dust. In an analysis of a sample of 38 Seyfert galaxies, inclusion of the hot dust component improves the model fit in approximately half the cases. All derived dust temperatures are below 300 K, in the range 540–860 K or above 1300 K. A noteworthy feature is the estimation of confidence intervals for the component contributions: this is achieved by bootstrapping. It is also pointed out that the model implies that such data could be fruitfully analysed in terms of principal components.     

EMERGY ANALYSIS OF GRAIN PRODUCTION SYSTEM IN JIANGSU AND SHAANXI PROVINCE

1INTRODUCTIONGrain production has always been top-priority issue con-cerned by government and people (MA etal., 2002). Af-ter China put household contract responsibility system in force, its domestic issue of grain was basically settled in the mid 1980s (MA and XU, 1999). However, in the days when idea of sustainable development is broadly accepted, efficiency and sustainability of grain produc-tion are now in question, which have prompted interest in reviewing grain production patterns…     

Generalized strain probing of constitutive models

Advanced material constitutive models are used to describe complex soil behaviour. These models are often used in the solution of boundary value problems under general loading conditions. Users and developers of constitutive models need to methodically investigate the represented soil response under a wide range of loading conditions. This paper presents a systematic procedure for probing constitutive models. A general incremental strain probe, 6D hyperspherical strain probe (HSP), is introduced to examine rate‐independent model response under all possible strain loading conditions. Two special cases of HSP, the true triaxial strain probe (TTSP) and the plane‐strain strain probe (PSSP), are used to generate 3‐D objects that represent model stress response to probing. The TTSP, PSSP and general HSP procedures are demonstrated using elasto‐plastic models. The objects resulting from the probing procedure readily highlight important model characteristics including anisotropy, yielding, hardening, softening and failure. The PSSP procedure is applied to a Neural Network (NN) based constitutive model. It shows that this probing is especially useful in understanding NN constitutive models, which do not contain explicit functions for yield surface, hardening, or anisotropy. Copyright © 2004 John Wiley & Sons, Ltd.     

概率赋范空间的S-凸和K-凸

文献[1][2]认为M－PN空间是局部凸的。通过定理说明这个结论对t-模T（a,b)≥min(a,b)时成立。又通过反例表明，当t-模型T（a,b)=max(a b-1,0）时无论邻域Nθ（ε，λ）概率有界还是概率半有界结论均不成立。还讨论了Takahashi凸性并对S－凸和K－凸作了比较。     

A high resolution,multiproxy Late‐glacial record of climate change and intrasystem responses in northwest England

A lacustrine carbonate sequence from Hawes Water, Lancashire, UK, has been studied using stable isotopic, lithological, pollen and mineral magnetic analysis. The data reveal four abrupt climatic oscillations in the Late‐glacial Interstadial leading up to the onset of the Loch Lomond Stadial. The data also point to climatic warming relatively early within the stadial, ca. 12 500 GRIP yr, prior to the onset of the Holocene. The oxygen isotope record is taken as a signature of climate forcing against which the response of the lake‐system can be monitored. By adopting this approach it is revealed that the response of the biological system to the rapid climatic oscillations is non‐linear and primarily a function of the antecedent conditions. A significant end‐Devensian isotopic excursion (A) is matched by only minor changes in the cold‐adapted floras and faunas. During the warmer interstadial, the response of the biological ecosystem (events B–D) is clearly influenced by thresholds: major changes in the catchment vegetation associated with relatively minor oscillations in the isotopic signature. The stratigraphical patterns reveal significant lag effects between the onset of climate deterioration and resulting changes in vegetation. Copyright © 2002 John Wiley & Sons, Ltd.     

Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.