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排序方式: 共有4237条查询结果,搜索用时 31 毫秒
1.
WANGXie-kang HUANGEr CUIPeng 《中国地理科学(英文版)》2003,13(3):262-266
Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-fill in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed. 相似文献
2.
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. 相似文献
3.
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. 相似文献
4.
Olac Fuentes 《Experimental Astronomy》2001,12(1):21-31
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. 相似文献
5.
Lin Liu 《Chinese Astronomy and Astrophysics》1997,21(4):488-500
With the aim of analyzing the errors in the radial position of satellites, we give in this paper the expressions for the radial perturbation which include the complete zeroth- and first-order terms in the orbital eccentricity. A simpler and faster method of calculating the perturbation is given. 相似文献
6.
Sunetra Giridhar N. Kameswara Rao David L. Lambert 《Journal of Astrophysics and Astronomy》1996,17(1-2):23-40
Two high resolution spectra of the hot RCrB star DY Cen in the red region are compared. The photospheric absorption lines
show a radial velocity variation of 12 kms-1 between 1989 July and 1992 May. Emission components to some CII lines present
in 1989 are almost entirely absent in 1992. Nebular forbidden lines of [OI], [NII] and [SII] appear unchanged from 1989 to
1992 相似文献
7.
R. F. Griffin 《Journal of Astrophysics and Astronomy》1990,11(4):533-540
Photoelectric radial-velocity measurements show that HD 118670 is a double-lined spectroscopic binary in an orbit which is
not quite circular and whose period is about 48 days. Spectral types of K0 V and K7 V would satisfy the photometry and the
mass ratio; the mass function would then suggest the possibility of eclipses. However, actual spectral classification indicates
a luminosity somewhat above the main sequence 相似文献
8.
In urban environments, one major concern with deep excavations in soft clay is the potentially large ground deformations in and around the excavation. Excessive movements can damage adjacent buildings and utilities. There are many uncertainties associated with the calculation of the ultimate or serviceability performance of a braced excavation system. These include the variabilities of the loadings, geotechnical soil properties, and engineering and geometrical properties of the wall. A risk‐based approach to serviceability performance failure is necessary to incorporate systematically the uncertainties associated with the various design parameters. This paper demonstrates the use of an integrated neural network–reliability method to assess the risk of serviceability failure through the calculation of the reliability index. By first performing a series of parametric studies using the finite element method and then approximating the non‐linear limit state surface (the boundary separating the safe and ‘failure’ domains) through a neural network model, the reliability index can be determined with the aid of a spreadsheet. Two illustrative examples are presented to show how the serviceability performance for braced excavation problems can be assessed using the reliability index. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
9.
Singh et al (2005) examined the potential of the ANN and neuro-fuzzy systems application for the prediction of dynamic constant of rockmass.
However, the model proposed by them has some drawbacks according to fuzzy logic principles. This discussion will focus on
the main fuzzy logic principles which authors and potential readers should take into consideration. 相似文献
10.
M. Irfan Yesilnacar Erkan Sahinkaya Muhsin Naz Bestamin Ozkaya 《Environmental Geology》2008,56(1):19-25
Monitoring groundwater quality by cost-effective techniques is important as the aquifers are vulnerable to contamination from
the uncontrolled discharge of sewage, agricultural and industrial activities. Faulty planning and mismanagement of irrigation
schemes are the principle reasons of groundwater quality deterioration. This study presents an artificial neural network (ANN)
model predicting concentration of nitrate, the most common pollutant in shallow aquifers, in groundwater of Harran Plain.
The samples from 24 observation wells were monthly analysed for 1 year. Nitrate was found in almost all groundwater samples
to be significantly above the maximum allowable concentration of 50 mg/L, probably due to the excessive use of artificial
fertilizers in intensive agricultural activities. Easily measurable parameters such as temperature, electrical conductivity,
groundwater level and pH were used as input parameters in the ANN-based nitrate prediction. The best back-propagation (BP)
algorithm and neuron numbers were determined for optimization of the model architecture. The Levenberg–Marquardt algorithm
was selected as the best of 12 BP algorithms and optimal neuron number was determined as 25. The model tracked the experimental
data very closely (R = 0.93). Hence, it is possible to manage groundwater resources in a more cost-effective and easier way with the proposed
model application. 相似文献