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A Hybrid Deep Stacked LSTM and GRU for Water Price Prediction

机译：混合深层LSTM和GRU进行水价预测

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Water pricing and freshwater scarcity is an emerging global issue, a topic of debate among researchers, households and water utility managers. This is due to the fact that, the process can provide early warning signs as well as assisting water utility managers to make proper decisions on control and management of the scarce water resources through implementing water pricing policies, ensuring proper water allocation, water-use restriction as well as water production. In this paper, we presented a two-step methodology coupled stacked LSTM+GRU models while analyzing their relative performance to our reference models i.e. stacked LSTM and GRU for long term water price Prediction. It is thought that, the coupled Stacked LSTM and GRU models to exploit building of higher level of representation of the input sequence data while creating a higher level of abstraction on the final results. The GRU on the other hand assists in solving the vanishing gradient problems. The experimental results obtained from this research work indicates our coupled (Stacked LSTM+GRU) with supervised learning to significantly outperform our reference models for water price Prediction.
机译：水价和淡水短缺是一个新兴的全球性问题，是研究人员，家庭和水务管理者之间争论的话题。这是由于以下事实：该过程可以提供预警信号，并通过实施水价政策，确保适当的用水分配，用水限制来协助水务管理者对稀缺水资源的控制和管理做出适当的决定。以及水的生产。在本文中，我们提出了两步方法，将堆叠的LSTM + GRU模型耦合在一起，同时分析了它们与我们的参考模型的相对性能，即用于长期水价预测的堆叠的LSTM和GRU。可以认为，耦合的LSTM和GRU堆叠模型可以利用构建更高级别的输入序列数据表示形式，同时在最终结果上创建更高级别的抽象。另一方面，GRU有助于解决消失的梯度问题。从这项研究工作获得的实验结果表明，我们在监督学习的基础上结合（堆叠的LSTM + GRU）大大优于我们的水价预测参考模型。

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