开发和实施用于模拟自然区域洪水现象的人工神经网络

E. Keramaris, Ioannis Petikas
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摘要

本研究开发了一个人工神经网络,用于模拟自然区域的洪水现象。然后,在希腊城市(弗洛里纳的 Amyntaio)的城区实施了该网络。神经网络有许多优点:非线性、适应性、输入输出映射、指示性响应、抗破坏性、可采用 VLSI(超大规模集成)技术、与内容相关的信息和分析以及设计统一性。使用神经网络时,并不试图对所考虑的现象进行数学模拟,而是根据类似案例,针对具体数据提取定量结论。通过该网络的开发和实施,可以确定所有洪水风险点。结果表明,在这些情况下,人工神经网络的帮助对未来洪水现象的决策至关重要。
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Development and Implementation of an Artificial Neural Network for the Simulation of Flood Phenomena in a Natural Area
In this study an Artificial Neural Network for the simulation of flood phenomena in a natural area was developed. Then this network was implemented in the urban area of a Greek city (Amyntaio, Florina). The neural networks have many advantages: non-linearity, adaptability, input-output mapping, indicative response, damage resistance, possibility of implementation with VLSI (Very Large Scale Integration) technology, content related information and analysis and design uniformity. With neural networks, mathematical simulation of the considered phenomenon is not attempted, but the extraction of quantitative conclusions for specific data, based on similar cases. With the development and implementation of this network all the points that are in risk for flood are identified. The results showed that the help of an Artificial Neural Network in these cases is crucial for the future decisions in cases of flood phenomena.
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