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首页> 外文期刊>Journal of Hydroinformatics >A Bayesian network-based data analytical approach to predict velocity distribution in small streams
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A Bayesian network-based data analytical approach to predict velocity distribution in small streams

机译：基于贝叶斯网络的数据分析方法来预测小溪流中的速度分布

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摘要

Developing a reliable data analytical method for predicting the velocity profile in small streams is important in that it substantially decreases the amount of money and effort spent on measurement procedures. In recent studies it has been shown that machine learning models can be used to achieve such an important goal. In the proposed framework, a tree-augmented Naive Bayes approach, a member of the Bayesian network family, is employed to address the aforementioned two issues. Therefore, the proposed study presents novelty in that it explores the relations among the predictor attributes and derives a probabilistic risk score associated with the predictions. The data set of four key stations, in two different basins, are employed and the eight observational variables and calculated non-dimensional parameters were utilized as inputs to the models for estimating the response values, u (point velocities in measured verticals). The results showed that the proposed data-analytical approach yields comparable results when compared to the widely used, powerful machine learning algorithms. More importantly, novel information is gained through exploring the interrelations among the predictors as well as deriving a case-specific probabilistic risk score for the prediction accuracy. These findings can be utilized to help field engineers to improve their decision-making mechanism in small streams.
机译：开发一种可靠的数据分析方法来预测小溪流中的速度曲线很重要，因为它可以大大减少在测量过程中花费的金钱和精力。在最近的研究中，已经表明机器学习模型可以用于实现这一重要目标。在提出的框架中，采用了贝叶斯网络家族成员之一的树增强朴素贝叶斯方法来解决上述两个问题。因此，所提出的研究具有新颖性，它探索了预测变量属性之间的关系，并得出了与预测相关的概率风险评分。使用了两个不同盆地中四个关键站的数据集，并将八个观测变量和计算出的无量纲参数用作模型的输入，以估计响应值u（测得的垂直方向上的点速度）。结果表明，与广泛使用的功能强大的机器学习算法相比，所提出的数据分析方法可产生可比的结果。更重要的是，通过探索预测变量之间的相互关系以及推导特定案例的概率风险评分来获得预测准确性，可以获得新颖的信息。这些发现可用于帮助现场工程师改进小流程中的决策机制。

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