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Modelling of reservoir water release decision using neural network and temporal pattern of reservoir water level

机译:基于神经网络和水位水位时间模式的油藏水释放决策模型

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摘要

The reservoir is one of flood mitigation methods that aim to reduce the effect of flood at downstream flood prone areas. At the same time the reservoir also serves other\udpurposes. Through modelling, how the reservoir operator made decisions in the past can be revealed. Consequently, the information can be used to guide reservoir operator making present decision especially during emergency situations such as flood and drought. This paper discussed modelling of reservoir water release decision using Neural Network (NN)and the temporal pattern of reservoir water level. Temporal pattern is used to represent the time delay as the rainfall upstream may not directly raise the reservoir water level. The flow of water may take some time to reach the reservoir due to the location. Seven NN models have been developed and tested. The findings show that the NN model with 5-25-1 architecture demonstrate the best performance compare to the other models.
机译:水库是减轻洪水影响的一种方法,旨在减少洪水在下游易发地区的影响。同时,水库还具有其他用途。通过建模,可以揭示储层运营商过去的决策方式。因此,该信息可用于指导水库运营商做出当前决策,尤其是在洪水和干旱等紧急情况下。本文讨论了使用神经网络(NN)进行储层水释放决策的建模以及储层水位的时间模式。时间模式用于表示时间延迟,因为上游降雨可能不会直接提高水库水位。由于位置的原因,水流可能需要一些时间才能到达水库。已经开发并测试了七个NN模型。研究结果表明,与其他模型相比,具有5-25-1架构的NN模型表现出最佳性能。

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