Abstract:
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.
