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首页> 外文期刊>Acta Geophysica Polonica >ARTIFICIAL NEURAL NETWORKS AS AN ALTERNATIVE TO THE VOLTERRA SERIES IN RAINFALL-RUNOFF MODELLING
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ARTIFICIAL NEURAL NETWORKS AS AN ALTERNATIVE TO THE VOLTERRA SERIES IN RAINFALL-RUNOFF MODELLING

机译：人工神经网络在降雨径流建模中作为Volterra系列的替代方法

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摘要

Methods of description of the non-linear effects in dynamic rainfall-runoff systems have been surveyed. Particular reference is given to such non-linear methods which do not require detailed topographical survey and determination of roughness parameters. To describe rainfall-runoff relation, alternative approaches to nonlinear partial differential equations of mass and energy transfer have been discussed, namely conceptual and black-box models. In more details, application of Volterra net, Multi-Layer Perceptron Artificial Neural Network and Radial Basis Function Network is tackled. Illustrative numerical examples of rainfall-runoff simulation and river flow forecast are presented.
机译：已经研究了描述动态降雨-径流系统中非线性效应的方法。特别参考了这样的非线性方法，它们不需要详细的地形调查和确定粗糙度参数。为了描述降雨-径流关系，已经讨论了质量和能量传递的非线性偏微分方程的替代方法，即概念模型和黑匣子模型。更详细地讲，解决了Volterra网络，多层感知器人工神经网络和径向基函数网络的应用。给出了降雨径流模拟和河流流量预报的说明性数值示例。

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