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首页> 外文期刊>Journal of hydrologic engineering >Reservoir Inflow Forecast Using a Clustered Random Deep Fusion Approach in the Three Gorges Reservoir, China
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Reservoir Inflow Forecast Using a Clustered Random Deep Fusion Approach in the Three Gorges Reservoir, China

机译：中国三峡水库利用聚类随机深度融合方法进行水库入库量预测

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摘要

Reservoir inflow forecast plays a crucial part in programming, development, operation, and management of water resource systems. To better reveal the complex properties of daily reservoir inflow, a clustered deep fusion (CDF) approach is proposed in this paper. First, variational mode decomposition (VMD) is used to decompose the daily reservoir inflow series into multiple modes, which are clustered into different sets by fuzzy c-means according to the Xie-Beni index in view of attribute domain. In each cluster, a deep autoencoder model (DAE) is developed for deep representations of the attributes in the deep domain. DAE outputs are finally fused at the synthesis domain into the forecasting results using random forest (RF). In this way, the inflow time series may be successively observed in the attribute domain, deep domain, and synthesis domain, which results in a clearer understanding of reservoir inflow trend. The present approach is modeled and evaluated using historical data collected from the Three Gorges Reservoir, China. For comparison, two kinds of learning patternsdeep learning (VMD-DAE-RF and DAE) and shallow learning (feed-forward neural network, least-squares support regression, and RF)are applied to the same case. The results indicate that the proposed CDF model outperforms all comparison models in terms of mean absolute percentage error (6.174%), root mean-square error (1,077.428m3/s), and correlation coefficient criteria (0.987). Thus, it is concluded that deep learning in the cluster fusion architecture is more promising.
机译：水库入库量预测在水资源系统的规划，开发，运营和管理中起着至关重要的作用。为了更好地揭示日储层的复杂性，本文提出了一种聚类深度融合（CDF）方法。首先，利用变分模式分解（VMD）将每日储层入渗序列分解为多种模式，并根据属性域根据Xie-Beni指数通过模糊c均值将其分为不同的集合。在每个群集中，都会开发一个深度自动编码器模型（DAE），用于深度域中属性的深度表示。最后，使用随机森林（RF）将DAE输出在合成域中融合到预测结果中。这样，可以在属性域，深层域和综合域中相继观察到流入时间序列，从而可以更清楚地了解储层流入趋势。使用从中国三峡水库收集的历史数据对本方法进行建模和评估。为了进行比较，将两种学习模式：深度学习（VMD-DAE-RF和DAE）和浅层学习（前馈神经网络，最小二乘支持回归和RF）应用于同一情况。结果表明，提出的CDF模型在平均绝对百分比误差（6.174％），均方根误差（1,077.428m3 / s）和相关系数标准（0.987）方面优于所有比较模型。因此，可以得出结论，在集群融合架构中进行深度学习更为有前途。

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