دانلود رایگان مقاله پیش بینی خطرات ساحلی برای سواحل شنی با شبکه بیزی

Abstract

Low frequency, high impact storm events can have large impacts on sandy coasts. The physical processes governing these impacts are complex because of the feedback between the hydrodynamics of surges and waves, sediment transport and morphological change. Predicting these coastal changes using a numerical model requires a large amount of computational time, which in the case of an operational prediction for the purpose of Early Warning is not available. For this reason morphodynamic predictions are not commonly included in Early Warning Systems (EWSs). However, omitting these physical processes in an EWS may lead to potential under or over estimation of the impact of a storm event. To solve this problem, a method has been developed to construct a probabilistic Bayesian Network (BN). This BN connects three elements: offshore hydraulic boundary conditions, characteristics of the coastal zone, and onshore hazards, such as erosion and overwash depths and velocities. The hydraulic boundary conditions are derived at a water depth of approximately 20 m from a statistical analysis of observed data using copulas, and site characteristics are obtained from measurements. This BN is trained using output data from many pre-computed process-based model simulations, which connect the three elements. Once trained, the response of the BN is instantaneous and can be used as a surrogate for a process-based model in an EWS in which the BN can be updated with an observation of the hydraulic boundary conditions to give a prediction for onshore hazards. The method was applied to Praia de Faro, Portugal, a low-lying urbanised barrier island, which is subject to frequent flooding. Using a copula-based statistical analysis, which preserves the natural variability of the observations, a synthetic dataset containing 100 events was created, based on 20 years of observations, but extended to return periods of significant wave height of up to 50 years. These events were transformed from offshore to onshore using a 2D XBeach (Roelvink et al., 2009) model. Three BN configurations were constructed, of which the best performing one was able to predict onshore hazards as computed by the model with an accuracy ranging from 81% to 88% and predict events with no significant onshore hazards with an accuracy ranging from 90% to 95%. Two examples are presented on the use of a BN in operational predictions or as an analysis tool. The added value of this method is that it can be applied to many coastal sites: (1) limited observations of offshore hydrodynamic parameters can be extended using the copula method which retains the original observations' natural variability, (2) the transformation from offshore observations to onshore hazards can be computed with any preferred coastal model and (3) a BN can be adjusted to fit any relevant connections between offshore hydraulic boundary conditions and onshore hazards. Furthermore, a BN can be continuously updated with new information and expanded to include different morphological conditions or risk reduction measures. As such, it is a promising extension of existing EWSs and as a planning tool for coastal managers.

5. Summary and conclusions

A method has been developed to construct a probabilistic Bayesian Network (BN), which acts as a surrogate for a process-based model, and can be used as part of an Early Warning System (EWS) for sandy coasts. The BN connects three elements: hydraulic boundary conditions at the 20 m depth contour, characteristics of the coastal zone, and onshore hazards. Hydraulic boundary conditions were derived from a statistical analysis of observed data using copulas, and site characteristics were obtained from measurements. This BN was trained using output data from many pre-computed process-based model simulations, which connect the three elements. Once trained, the response of the BN is instantaneous and can be applied in operational mode as a surrogate for the processbased model. As part of an EWS, the BN can be updated with an observation of the hydraulic boundary conditions to give a prediction for onshore hazards such as erosion, overwash depth and velocities. As an analysis tool, the BN can be used to assess the effect of constraining the probability of one or more variables on the rest of the network. The method was applied to Praia de Faro, Portugal, a low-lying urbanised barrier island, which is subject to frequent flooding. Using a copula-based statistical analysis, which preserves the natural variability of the observations, a synthetic dataset containing 100 events was created, based on 20 years of observations, but extended to return periods of significant wave height of up to 50 years. These events, characterised by the significant wave height, peak wave period, maximum water level and the storm duration, were transformed from offshore to onshore using a 2D XBeach (Roelvink et al., 2009) model. The onshore hazard intensities which are predicted are erosion, overwash depth and flow velocity. Three BNs configurations were constructed, of which the best performing one was able to predict onshore hazards as computed by the model with an accuracy ranging from 81% to 88% and predict no significant onshore hazards with an accuracy ranging from 90% to 95%. Two examples were presented on the use of a BN in operational predictions or as an analysis tool. The added value of this method is that it generic enough to be applied to other coastal sites. Thus, BNs are a promising extension of existing EWSs and a valuable planning tool for coastal managers.