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首页> 外文期刊>Journal of Hydroinformatics >The extrapolation of artificial neural networks for the modelling of rainfall-runoff relationships
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The extrapolation of artificial neural networks for the modelling of rainfall-runoff relationships

机译：人工神经网络的外推法模拟降雨-径流关系

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The last decade has seen increasing interest in the application of Artificial Neural Networks (ANNs) for the modelling of the relationship between rainfall and streamflow. Since multi-layer, feed-forward ANNS have the property of being universal approximators, they are able to capture the essence of most input-output relationships, provided that an underlying deterministic relationship exists, unfortunately, owing to the standardisation of inputs and outputs that is required to run ANNS, a problem arises in extrapolation: if the training data set does not contain the maximum possible output value, an unmodified network will be unable to synthesise this peak value. The occurrence of high magnitude, low frequency events within short periods of record is largely fortuitous. Therefore, the confidence in the neural network model can be greatly enhanced if some methodology can be found for incorporating domain knowledge about such events into the calibration and verification procedure in addition to the available measured data sets. One possible form of additional domain knowledge is the Estimated Maximum Flood (EMF), a notional event with a small but non-negligible probability of exceedence. This study investigates the suitability of including an EMF estimate in the training set of a rainfall-runoff ANN in order to improve the extrapolation characteristics of the network. A study has been carried out in which EMFs have been included, along with recorded flood events, in the training of ANN models for six catchments in the south west of England. The results demonstrate that, with prior transformation of the runoff data to logarithms of flows, the inclusion of domain knowledge in the form of such extreme synthetic events improves the generalisation capabilities of the ANN model and does not disrupt the training process. Where guidelines are available for EMF estimation, the application of this approach is recommended as an alternative means of overcoming the inherent extrapolation problems of multi-layer, feed-forward ANNs.
机译：在过去的十年中，人们越来越多地将人工神经网络（ANN）用于降雨和流量之间关系的建模。由于多层前馈ANNS具有作为通用逼近器的特性，因此不幸的是，由于存在潜在的确定性关系，因此由于输入和输出的标准化，它们可以捕获大多数输入-输出关系的本质。如果需要运行ANNS，则外推会出现问题：如果训练数据集不包含最大可能的输出值，则未经修改的网络将无法合成该峰值。在记录的短时间内发生高强度，低频事件在很大程度上是偶然的。因此，如果可以找到一些方法来将关于此类事件的领域知识并入到校准和验证过程中，并且可以找到可用的测量数据集，那么可以大大提高神经网络模型的置信度。额外领域知识的一种可能形式是估计最大洪水（EMF），这是一种名义事件，其发生概率很小，但不可忽略。这项研究调查了在降雨径流人工神经网络的训练集中包括EMF估计值的适用性，以改善网络的外推特性。已经进行了一项研究，其中包括在英格兰西南部六个流域的ANN模型训练中包括EMF和已记录的洪水事件。结果表明，通过将径流数据事先转换为流量的对数，以此类极端综合事件的形式包含领域知识可以提高ANN模型的泛化能力，并且不会干扰训练过程。如果有可用于EMF估算的指南，则建议采用此方法作为克服多层前馈ANN固有外推问题的替代方法。

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