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- 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.
- Development of a Bayesian networks-based early warning system for wave-induced floodingPublication . Garzon, Juan L.; Ferreira, Óscar; Zózimo, A. C.; Fortes, C. J. E. M.; Ferreira, A. M.; Pinheiro, L. V.; Reis, M. T.Coastal flooding prediction systems can be an efficient risk-reduction instrument. The goal of this study was to design, build, test, and implement a wave-induced flooding early warning system in urban areas fronted by sandy beaches. The system utilizes a novel approach that combines Bayesian Networks and numerical models (SWAN + XBeach) and was developed in two phases. In the development phase, firstly, the learning information was generated including the creation of oceanic conditions, modeling overtopping discharges, the characterization of the associated impacts (no, low, moderate and high) in pedestrians, urban components and buildings, and vehicles, and secondly, the Bayesian Networks were designed that surrogated the previously generated information. After their training, the conditional probability tables were created representing the foundation to make predictions in the operational phase. This methodology was validated for several historical events which hit the study area (Praia de Faro, Portugal), and the system correctly predicted the impact level of around 80% of the cases. Also, the predictive skills varied depending on the level, with the no and high impact levels overcoming the intermediate levels. In terms of efficiency, one simulation (deterministic) of coastal flooding for 72 h by running SWAN + XBeach operationally would take more than two days on a one-logical processor workstation, while the current approach can provide quasi-instantaneously predictions for that period, including probability distributions. Moreover, the two-working phase approach is very flexible enabling the inclusion of additional features such as social components representing a powerful tool for risk reduction in coastal communities.
- Conceptual and quantitative categorization of wave-induced flooding impacts for pedestrians and assets in urban beachesPublication . Garzon, Juan L.; Ferreira, Ó.; Reis, M. T.; Ferreira, A.; Fortes, C. J. E. M.; Zózimo, A. C.Coastal fooding is a major threat to communities living in low-lying areas and the increase in the anthropogenic pressure in coastal zones and the efects of climate change (e.g., sea-level rise, increase in storminess and its frequency) are promoting an enhancement of the existing risks for population and properties 1–4 . Coastal fooding results from the interaction of oceanic and atmospheric processes with the local and regional features (topography, nearshore bathymetry, continental shelf, and land use). Among the diferent oceanic agents that might drive coastal fooding, wave-related processes have been found to be the dominant component in large areas of the globe compared to storm surges and tides 5 . When waves approach the shoreline, a large part of the wave energy is dissipated across the surf zone by wave breaking. However, a portion of the remaining energy is converted to potential energy in the form of wave runup on the beach foreshore 6 contributing to boosting the extreme water levels 3 . When the existing natural or man-made coastal protection structure (constructed on land) is lower than the maximum level that water can reach by wave attack, a discharge occurs over the structure and propagates inland. It can be called green water 7 (non-impulsive), when a layer of water passes over the crest, or white water 7 (or impulsive conditions) when waves break on the seaward face of the structure and produce signifcant volumes of splash or spray (not considered here). Terefore, wave runup (and overtopping) is important to coastal planners and engineers because it delivers much of the energy responsible for causing a fooding event 8. Besides disruptions in local services and transportation, during such events, seawater can travel with high velocities, which in turn can afect the integrity of urban elements and properties, and severely injure people.