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首页> 外文期刊>Hydrology research >Crisp discharge forecasts and grey uncertainty bands using data-driven models
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Crisp discharge forecasts and grey uncertainty bands using data-driven models

机译：使用数据驱动模型的排放预测和灰色不确定带

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摘要

A data-driven artificial neural network (ANN) model and a data-driven evolutionary polynomial regression (EPR) model are here used to set up two real-time crisp discharge forecasting models whose crisp parameters are estimated through the least-square criterion. In order to represent the total uncertainty of each model in performing the forecast, their parameters are then considered as grey numbers. Comparison of the results obtained through the application of the two models to a real case study shows that the crisp models based on ANN and EPR provide similar accuracy for short forecasting lead times; for long forecasting lead times, the performance of the EPR model deteriorates with respect to that of the ANN model. As regards the uncertainty bands produced by the grey formulation of the two data-driven models, it is shown that, in the ANN case, these bands are on average narrower than those obtained by using a standard technique such as the Box-Cox transformation of the errors; in the EPR case, these bands are on average larger. These results therefore suggest that the performance of a grey data-driven model depends on its inner structure and that, for the specific models here considered, the ANN is to be preferred.
机译：本文使用数据驱动的人工神经网络（ANN）模型和数据驱动的进化多项式回归（EPR）模型来建立两个实时的脆性流量预测模型，其脆性参数是通过最小二乘法标准估算的。为了表示执行预测时每个模型的总不确定性，然后将它们的参数视为灰度数字。通过将这两个模型应用于实际案例的结果比较表明，基于ANN和EPR的清晰模型可以在较短的交货周期内提供相似的准确性。对于较长的交货时间预测，相对于ANN模型，EPR模型的性能会下降。关于由两个数据驱动模型的灰色表述所产生的不确定带，表明在ANN情况下，这些带平均比使用标准技术（如Box-Cox变换）获得的窄。错误；在EPR情况下，这些频段平均更大。因此，这些结果表明，灰色数据驱动模型的性能取决于其内部结构，对于此处考虑的特定模型，应首选ANN。

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