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A quadtree-based image segmentation procedure (HQ) is presented to map complex environmental conditions. It applies a hierarchical nested analysis of variance within the framework of multiresolution wavelet approximation. The procedure leads to an optimal solution for determining mapping units based on spatial variability with constraints on the arrangement and shape of the units. Linkages to geostatisiics are pointed out, but the HQ decomposition algorithm does not require any homogeneity criteria. The computer implementation can be parameterized by either the number of required mapping units or the maximum within-unit variance, or it can provide a spectrum of significances of nested ANOVA. The detailed mathematical background and methodology is illustrated by a salt-affected grassland mapping study (Hortobágy, Hungary), where heterogeneous environmental characteristics have been sampled and predicted based on remotely sensed images using these principles. 相似文献
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In integrated assessments of climate change, greenhouse-gasemissions and climate change impacts provide the linkages betweenthe world economy and the climate system. Key climatic processesoperate at the scales of centuries. This requires highlyaggregated models for portraying the dynamics of the economicsystem. An extended Ramsey-type optimal growth model is presentedas the appropriate tool to be integrated with a reduced-formclimate model in the ICLIPS integrated assessment. Theeleven-region model of the world economy involves exogenouspopulation and endogenous investment dynamics with productivityprogress based on a technological diffusion model. World regionsare linked via intertemporal trade flows of the compositeconsumption/investment good, capital mobility, and emission permittrading. Coupled with the ICLIPS Climate Model, the AggregatedEconomic Model can determine corridors of permitted long-termcarbon emission paths or, as primarily discussed in this paper,specific cost-effective emission trajectories. The sensitivity ofmitigation costs to externally specified climate change/impactconstraints and to assumptions about non-CO2 greenhouse-gasemissions is also discussed. 相似文献
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The volume and the photosensitive area of next generation detectors of the numerous rarely occurring phenomena will greatly exceed the sizes of the current experiments. These phenomena include cosmic neutrinos, atmospheric neutrinos, long-baseline neutrino beams from accelerators, geo-neutrinos, geo-reactor neutrinos, and hypothetic proton decays. Similar requirements hold for a new type of a large scanning device for homeland security and nuclear proliferation control, and for the future widely accessible medical imaging devices. Photon detectors are the most important component of such detectors. Existing photosensors are based on vacuum tubes and dynode electron multipliers that are essentially hand-made, expensive and nearly impossible to produce in large enough quantities. Silicon detectors are too small for experiments requiring a very large photosensitive area. Our laboratory is developing novel detectors with a large photosensitive area that can be mass-produced, similar to large flat panel TV displays. 相似文献
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Numerical models constitute the most advanced physical-based methods for modeling complex ground water systems. Spatial and/or temporal variability of aquifer parameters, boundary conditions, and initial conditions (for transient simulations) can be assigned across the numerical model domain. While this constitutes a powerful modeling advantage, it also presents the formidable challenge of overcoming parameter uncertainty, which, to date, has not been satisfactorily resolved, inevitably producing model prediction errors. In previous research, artificial neural networks (ANNs), developed with more accessible field data, have achieved excellent predictive accuracy over discrete stress periods at site-specific field locations in complex ground water systems. In an effort to combine the relative advantages of numerical models and ANNs, a new modeling paradigm is presented. The ANN models generate accurate predictions for a limited number of field locations. Appending them to a numerical model produces an overdetermined system of equations, which can be solved using a variety of mathematical techniques, potentially yielding more accurate numerical predictions. Mathematical theory and a simple two-dimensional example are presented to overview relevant mathematical and modeling issues. Two of the three methods for solving the overdetermined system achieved an overall improvement in numerical model accuracy for various levels of synthetic ANN errors using relatively few constrained head values (i.e., cells), which, while demonstrating promise, requires further research. This hybrid approach is not limited to ANN technology; it can be used with other approaches for improving numerical model predictions, such as regression or support vector machines (SVMs). 相似文献
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Ádám Leelőssy Ferenc Molnár Jr. Ferenc Izsák Ágnes Havasi István Lagzi Róbert Mészáros 《Central European Journal of Geosciences》2014,6(3):257-278
Modeling of dispersion of air pollutants in the atmosphere is one of the most important and challenging scientific problems. There are several natural and anthropogenic events where passive or chemically active compounds are emitted into the atmosphere. The effect of these chemical species can have serious impacts on our environment and human health. Modeling the dispersion of air pollutants can predict this effect. Therefore, development of various model strategies is a key element for the governmental and scientific communities. We provide here a brief review on the mathematical modeling of the dispersion of air pollutants in the atmosphere. We discuss the advantages and drawbacks of several model tools and strategies, namely Gaussian, Lagrangian, Eulerian and CFD models. We especially focus on several recent advances in this multidisciplinary research field, like parallel computing using graphical processing units, or adaptive mesh refinement. 相似文献
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The optimal sequencing of a multipurpose water supply system in the Hajduhátság region of Hungary is determined by dynamic programming. The goal function minimizes the present value of capital costs, operation costs, and economic losses due to water shortages. Future water requirements are considered to be random variables because of natural and forecasting uncertainties. The nonlinear optimization problem at each stage is equivalent to a readily solved game theoretical problem, the solution of which is straightforward. Sensitivity analysis performed with respect to economic losses, water requirements and discount rate, showed that optimal development and sequencing depend largely on the economic losses and the discount rate. 相似文献
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Laštovička Jan Burešová Dalia Boška Josef Bremer Jurgen Märcz Ferenc 《Studia Geophysica et Geodaetica》1997,41(2):171-180
Data from four A3 radio wave circuits in central Europe are used to analyse the representativeness of the ionization and gravity wave activity pattern in the lower ionosphere (85 – 100 km) in the CRISTA experiment interval (3 – 12 November 1994) for the given conditions. It has been found that the CRISTA experiment interval was run under conditions, which are highly representative both of October – November 1994 and autumn, low-moderate solar activity, the descending phase of solar cycle conditions, i.e. CRISTA measurements may be considered to provide values identical with, or close to climatological values (at least for central Europe, h = 85 – 100 km). 相似文献