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A Bayesian network approach to study hydromorphological modifications over space and time in the framework of a sustainable river restoration project: the 'Lac des Gaves' case study

机译:一种贝叶斯网络方法,用于研究可持续河恢复项目框架空间和时间的水平修饰:“Lac des Gaves”案例研究

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The "Lac des Gaves", an artificial lake located in the main stream of the "Gave de Pau" river in the high Pyrenees, has gone through very intensive sediment extractions over the past century. This resulted in the lake acting like a sediment trap causing a brutal longitudinal profile discontinuity that leads to increasing safety and environmental risks. Considering the multi-criteria character of this study, a deep analysis of the lake's historical evolution until its current situation is needed to be able to propose sustainable restoration solutions. In order to understand the current situation and predict the future behavior of the study area after its restoration, we decided to analyze, in a complementary way, its historical and experimental hydromorphological characteristics to help design well suited solutions. To be able to include uncertainties in the qualification of the morphological trajectory from a period to another, identify the causes of a given modification, analyze their potential impact on the studied system and fill the data gaps by expert knowledge, a probabilistic approach supported by Bayesian Belief Networks (BBNs) has been chosen. BBNs are increasingly being used as tools for decision-making in river management because they can adjust to complex multi-criteria systems with multiple interactions like the ones we consider in this project. They can be divided in two components: a causal graph that illustrates the qualitative relationships between the variables and the quantitative description of these relationships thanks to Conditional Probability Tables (CPTs). The approach considered in this study will rely on the capacity to feed the models with information collected on the ground combined with physical knowledge on the phenomena. This part of the project is hence consistent with the approach presented above. The final goal would be to extrapolate this method to other watercourses going through the same kinds of pressures.
机译:“Lac des Gaves”,一个位于高比利牛斯的“Gave de Pau”河流主流的人工湖,在过去的世纪经历了非常强烈的沉积物。这导致了湖泊表现得像沉积物陷阱,导致残酷的纵向曲线不连续,导致安全性和环境风险增加。考虑到这项研究的多标准特征,对湖泊的历史演变进行了深入的分析,直到其目前的情况是能够提出可持续恢复解决方案。为了了解目前的情况并预测恢复后研究区域的未来行为,我们决定以互补的方式分析其历史和实验性水平特征,以帮助设计良好的解决方案。为了能够在从一段时间内包括形态轨迹的资格中的不确定性,确定给定修改的原因,分析他们对研究系统的潜在影响,并通过专业知识填补数据差距,贝叶斯支持的概率方法信仰网络(BBNS)已被选中。 BBN越来越多地被用作河流管理中决策的工具,因为它们可以调整到复杂的多标准系统,其中具有多种相互作用,如我们考虑在该项目中。它们可以分为两个组件:由于条件概率表(CPTS)表示变量与这些关系的定量描述之间的定性关系的因果图。本研究中考虑的方法将依赖于在地面上收集的信息馈送模型的能力,与现象的身体知识相结合。因此,该项目的这一部分与上面呈现的方法一致。最终目标是将这种方法推断给经过同类压力的其他水道。

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