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首页> 外文期刊>Journal of Hydroinformatics >Air demand in gated tunnels - a Bayesian approach to merge various predictions
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Air demand in gated tunnels - a Bayesian approach to merge various predictions

机译：门控隧道中的空气需求-合并各种预测的贝叶斯方法

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摘要

High flowrate through gated tunnels may cause critical flow conditions, especially downstream of the regulatinggates. Aeration is found to be the most effective and efficient way to prevent cavitation attack. Several experimental equations are presented to predict air demand in gated tunnels; however, they are restricted to particular model geometries and flow conditions and often provide differing results. In this study the current relationships are first evaluated, and then other approaches for air discharge estimation are investigated. Three machine learning techniques are compared based on the flow measurements of eight physical models, with scales ranging from 1:12-1:20, including the fuzzy inference system (FIS), the genetic fuzzy system (GFS), and the adaptive network-based fuzzy inference system (ANFIS). The Bayesian Model Average (BMA) method is then proposed as a tool to merge the simulations from all models. The BMA provides the weighted average of the predictions, by assigning weights to each model in a probabilistic approach. The application of the BMA is found to be useful for improving the design of hydraulic structures by combining different models and experimental equations.
机译：通过门控隧道的高流量可能会导致严重的流量状况，尤其是在调节闸门的下游。人们发现通气是防止气蚀的最有效方法。提出了几个实验方程来预测门控隧道中的空气需求。但是，它们仅限于特定的模型几何形状和流动条件，并且通常会提供不同的结果。在这项研究中，首先评估了当前的关系，然后研究了其他的排气量估算方法。根据八个物理模型的流量测量结果，对三种机器学习技术进行了比较，其比例范围为1：12-1：20，包括模糊推理系统（FIS），遗传模糊系统（GFS）和自适应网络-基于模糊推理系统（ANFIS）。然后提出了贝叶斯平均模型（BMA）方法，作为合并来自所有模型的仿真的工具。 BMA通过以概率方法为每个模型分配权重来提供预测的加权平均值。通过结合不同的模型和实验方程，发现BMA的应用对于改进水工结构的设计是有用的。

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