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An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition1

机译：结合小波分解的人工神经网络降雨径流预报方法探讨1

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This study compares the one-day-ahead stream flow forecasting performance of multiple-layer artificial neurons and a neuro-wavelet hybrid system at two sites. Morlet power spectra are used to identify the period-scale structure of the available rainfall and runoff time series. The time series are wavelet decomposed into three sub-series depicting the rainfall-runoff processes: short, intermediate, and long wavelet periods. Then, multiple-layer artificial neurons are trained for each wavelet sub-series. Results show that the short wavelet periods are responsible for most of the final neuro-wavelet hybrid forecasting error. Short period fluctuations are thus the key to any further improvements in artificial neural network (ANN) rainfall-runoff forecasting models. The final performance of the neuro-wavelet hybrid forecasting system and of the classic forecasting multiple-layer artificial neuron system is very similar. The slight advantage in performance of the neuro-wavelet system may be attributed to a better usage of the evapotranspiration time series.
机译：这项研究比较了多层人工神经元和两个地方的神经小波混合系统的提前一天流量预测性能。 Morlet功率谱用于确定可用降雨和径流时间序列的周期尺度结构。时间序列被小波分解为三个子序列，描述了降雨径流过程：短，中和长小波周期。然后，为每个小波子系列训练多层人工神经元。结果表明，短小波周期是造成最终最终神经小波混合预测误差的主要原因。因此，短期波动是进一步改进人工神经网络（ANN）降雨径流预报模型的关键。神经小波混合预测系统和经典预测多层人工神经元系统的最终性能非常相似。神经小波系统在性能上的轻微优势可以归因于蒸散时间序列的更好利用。

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