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- Uncertainty analysis related to beach morphology and storm duration for more reliable early warning systems for coastal hazardsPublication . Garzon, Juan L.; Plomaritis, T. A.; Ferreira, OscarEarly warning systems (EWSs) for coastal erosion are highly cost-effective instruments for disaster risk reduction. Among other aspects, an adequate pre-storm beach morphology and the storm characteristic definition are relevant in determining EWSs prediction reliability. Here, XBeach simulations were used to investigate the beach-dune response to different storm events with varying duration and pre-storm morphologies. Severity was defined using wave height return periods (from 5 to 50 years) and duration variability was established by confidence intervals after an adjustment with wave height. Beach morphology variability included different berm morphologies, including erosional and accretional conditions. Three erosion indicators were used: remaining berm width, dune retreat, and eroded volume. Regarding the pre-storm morphology variability: i) pre-storm conditions highly determined the final berm width for the 5 and 10-year events; ii) antecedent morphology affected dune retreat variability mostly for the 50 year events, and; iii) eroded volume depended on the pre-storm conditions, but the percentage of the eroded volume, relative to the initial conditions, was similar regardless of the morphology. Regarding the storm duration effect: i) this variable had a limited impact on the remaining berm width for the 5-year event; ii) storm duration influenced dune retreat mainly for the 50-year event, determining dune breaching occurrence, and; iii) eroded volume response to changes in duration was similar regardless of storm intensity, except for the 50-year event. According to the obtained results, the implementation of reliable EWSs for coastal erosion needs to assess the uncertainties related to initial/forcing conditions, namely pre-storm morphology and storm duration.
- Development of a Bayesian network-based early warning system for storm-driven coastal erosionPublication . L. Garzon, Juan; Ferreira, Óscar; Plomaritis, T. A.; Zózimo, A. C.; Fortes, C. J. E. M.; Pinheiro, L. V.Coastal hazards such as flooding and erosion can cause large economic and human losses. Under this threat, early warning systems can be very cost-effective solutions for disaster preparation. The goal of this study was to develop, test, and implement an operational coastal erosion early warning system supported by a particular method of machine learning. Thus, the system combines Bayesian Networks, and state-of-the-art numerical models, such as XBeach and SWAN, to predict storm erosion impacts in urbanized areas. This system was developed in two phases. In the development phase, all information required to apply the machine learning method was generated including the definition of hundreds of oceanic synthetic storms, modeling of the erosion caused by these storms, and characterization of the impact levels according to a newly defined eerosion iimpact index. This adimensional index relates the distance from the edge of the dune/beach scarp to buildings and the height of that scarp. Finally, a Bayesian Network that acted as a surrogate of the previously generated information was built. After the training of the network, the conditional probability tables were created. These tables constituted the ground knowledge to make the predictions in the second phase. This methodology was validated (1) by comparing 6-h predictions obtained with the Bayesian Network and with process-based models, the latest considered as the benchmark, and (2) by assessing the predictive skills of the Bayesian Network through the unbiased iterative k-fold cross-validation procedure. Regarding the first comparison, the analysis considered the entire duration of three large storms whose return periods were 10, 16, and 25 years, and it was observed that the Bayesian Network correctly predicted between 64% and 72% of the impacts during the course of the storms, depending on the area analyzed. Importantly, this method was also able to identify when the hazardous conditions disappeared after predicting potential consequences. Regarding the Regarding the second validation approach, second validation approach, the k-fold cross-validation procedure was applied to the peak of a set of varying storms and it demonstrated that the predictive skills were maximized (63%-72%) when including three nodes as input conditions of the Bayesian Network. In the operational phase, the system was integrated into the architecture of a forecast and early warning system that predicts emergencies in coastal and port zones in Portugal, and the alerts are issued to authorities every day. This study demonstrated that the two-phase approach developed here can provide fast and high-accuracy predictions of erosion impacts. Also, this methodology can be easily implemented on other sandy beaches constituting a powerful tool for disaster management.