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Titlebook: Artificial Neural Networks in Hydrology; R. S. Govindaraju,A. Ramachandra Rao Book 2000 Springer Science+Business Media B.V. 2000 artifici

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發(fā)表于 2025-3-21 18:54:42 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Artificial Neural Networks in Hydrology
影響因子2023R. S. Govindaraju,A. Ramachandra Rao
視頻videohttp://file.papertrans.cn/163/162677/162677.mp4
學(xué)科分類Water Science and Technology Library
圖書封面Titlebook: Artificial Neural Networks in Hydrology;  R. S. Govindaraju,A. Ramachandra Rao Book 2000 Springer Science+Business Media B.V. 2000 artifici
影響因子R. S. GOVINDARAJU and ARAMACHANDRA RAO School of Civil Engineering Purdue University West Lafayette, IN. , USA Background and Motivation The basic notion of artificial neural networks (ANNs), as we understand them today, was perhaps first formalized by McCulloch and Pitts (1943) in their model of an artificial neuron. Research in this field remained somewhat dormant in the early years, perhaps because of the limited capabilities of this method and because there was no clear indication of its potential uses. However, interest in this area picked up momentum in a dramatic fashion with the works of Hopfield (1982) and Rumelhart et al. (1986). Not only did these studies place artificial neural networks on a firmer mathematical footing, but also opened the dOOf to a host of potential applications for this computational tool. Consequently, neural network computing has progressed rapidly along all fronts: theoretical development of different learning algorithms, computing capabilities, and applications to diverse areas from neurophysiology to the stock market. . Initial studies on artificial neural networks were prompted by adesire to have computers mimic human learning. As a result, the
Pindex Book 2000
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The metal-insulator transition in Si:P,cal to answering questions posed by management. These questions range from “what are the pathways by which receptors may be exposed to (human or ecological) health risks” to “can I determine from existing measurement data whether a buried facility is failing”.
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Hans Peter Wagner,Hermann Leidererrunoff and storage. The space-time distribution of soil moisture can influence atmospheric system on time scales from several hours to many years. Currently, the most feasible procedure to obtain soil moisture over large areas is through remote sensing.
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Optical characterization of ZnTe epilayers,ly to lakes, rivers and sub-surface moisture storage, which are the primary sources of water supply for plants and animals. Therefore, a better understanding of the temporal and spatial distribution of precipitation is critical to long-term water resources and agricultural system planning.
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