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

Yassine, Rabab; Pérès, François; Roux, Hélène; Cassan, Ludovic; Frysou, Olivier

doi:10.1016/j.ifacol.2018.06.191

Cited by 3 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bayes theorem is central in the mechanism of inference in BNs. It makes the link between a series of hypotheses, characterized by probabilities of occurrence, and a series of observations representing the actual state of the system (Liu, Tchangani, & Pérès, 2016;Villeneuve, Béler, Pérès, & Geneste, 2011;Yassine, Pérès, Roux, Cassan, & Frysou, 2018) • possibility to introduce decision and utility nodes in the model to assess the trustworthiness of different alternatives or scenarios.…”

Section: Bayesian Network As Supporting Toolmentioning

confidence: 99%

Participatory Bayesian modelling for sustainable and efficient river restoration projects: Feedback from the case study of the Gave de Pau River, Hautes‐Pyrénées, France

Yassine

Pérès

Frysou³

et al. 2020

Contingencies & Crisis Mgmt

Self Cite

View full text Add to dashboard Cite

Through the diversity of criteria and stakes, the uncertain nature of the entailed phenomena and the multi‐scale aspects to be taken into account, a river restoration project can be considered as a complex problem. Integrative approaches and modelling tools are thus needed to help river managers make predictions on the evolution of hydromorphological, socio‐economic, safety and ecological issues. Such approach can provide valuable information for handling long‐term management plans that consider the interaction and the balance of stakeholders interests and river system functioning. In this paper, we present a probabilistic participatory modelling (PM) method that assesses the effects of given restoration actions, knowing the hydromorphological modifications that they may induce on the safety, ecological and socio‐economic aspects with the help of local stakeholders through several workshops. To support this strategy, we used Bayesian networks (BNs) as modelling tools as their causal graphs can combine multidimensional knowledge and data from diverse natures. We introduce the causal graphs elaborated with the help of the stakeholders and convert it into BNs that can assist restoration decisions by considering the available decision and utility functions to provide guidance to decision‐makers. This was applied to the “Lac des Gaves” reach in the Hautes‐Pyrénées, France, a reach that has gone through severe sediment extractions for over 50 years. Each network represents possible restoration decisions linked to one of the observed issues. The paper demonstrates how BNs used as a decision support system (DSS) can help to assess the influence of given management strategies on the river system with the consideration of stakeholders’ knowledge and integration in all the modelling process.

show abstract

Section: Bayesian Network As Supporting Toolmentioning

confidence: 99%

Participatory Bayesian modelling for sustainable and efficient river restoration projects: Feedback from the case study of the Gave de Pau River, Hautes‐Pyrénées, France

Yassine

Pérès

Frysou³

et al. 2020

Contingencies & Crisis Mgmt

Self Cite

View full text Add to dashboard Cite

show abstract

“…These methods are independent of the complexity of the system under study, but require databases that are sufficiently representative to provide a trustworthy result. If this condition is met, the use of models based on Bayesian approaches makes sense (Garrote et al, 2007, Yassine, Pérès, Roux, Cassan, & Frysou, 2018, Yassine, Roux, et al, 2018). With the aim of assessing the flow discharge at a given location, they can take into account individual gauge uncertainties in the form of probabilistic distributions.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven model for river flood forecasting based on a Bayesian network approach

Boutkhamouine

Roux

Pérès

2020

Contingencies & Crisis Mgmt

Self Cite

View full text Add to dashboard Cite

Uncertainty analysis of hydrological models often requires a large number of model runs, which can be time consuming and computationally intensive. In order to reduce the number of runs required for uncertainty prediction, Bayesian networks (BNs) are used to graphically represent conditional probability dependence between the set of variables characterizing a flood event. Bayesian networks (BNs) are relevant due to their capacity to handle uncertainty, combine statistical data and expertise and introduce evidences in real‐time flood forecasting. In the present study, a runoff–runoff model is considered. The discharge at a gauging station located is estimated at the outlet of a basin catchment based on discharge measurements at the gauging stations upstream. The BN model shows good performances in estimating the discharges at the basin outlet. Another application of the BN model is to be used as a reverse method. Knowing discharges values at the outlet of the basin, we can propagate back these values through the model to estimate discharges at upstream stations. This turns out to be a practical method to fill the missing data in streamflow records which are critical to the sustainable management of water and the development of hydrological models.

show abstract

Lessons learnt from the application of a participatory modelling approach in the framework of a river restoration project: case of the Gave de Pau River, Hautes-Pyrénées, France

Yassine

Pérès

Frysou³

et al. 2020

IFAC-PapersOnLine

View full text Add to dashboard Cite

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

Cited by 3 publications

References 9 publications

Participatory Bayesian modelling for sustainable and efficient river restoration projects: Feedback from the case study of the Gave de Pau River, Hautes‐Pyrénées, France

Participatory Bayesian modelling for sustainable and efficient river restoration projects: Feedback from the case study of the Gave de Pau River, Hautes‐Pyrénées, France

Data‐driven model for river flood forecasting based on a Bayesian network approach

Lessons learnt from the application of a participatory modelling approach in the framework of a river restoration project: case of the Gave de Pau River, Hautes-Pyrénées, France

Contact Info

Product

Resources

About