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Tsunami is one of the real feelings of dread among humanity. Designing an early and effective Tsunami Warning System (TWS) is an immediate goal, for which the scientific community is working. Underwater seismic responses sensed by different numerical expository techniques have resulted in various cautionary frameworks proving successful in predicting tsunamis. However, multiple instances in the past where these warning systems have failed to generate alerts in time, has raised concerns to<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3022865">doi:10.1109/access.2020.3022865</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/b4ie33saczeylfiz5wp7junsxa">fatcat:b4ie33saczeylfiz5wp7junsxa</a> </span>
more »... even more efficient, diverse, and multidisciplinary warning methods or systems. However, there have been many instances in the past where these warning systems have failed to generate alerts in time, raising concerns about designing/implementing more efficient, diverse, and multidisciplinary warning methods or systems. Therefore, we propose a sequenced (EC G F C ) approach for designing a TWS, based on Ensemble Clustering (EC G ) and Classification for categorizing anomalous behavior in response to seismic perturbations, taking three aquatic animal behavioral datasets: Turtle, Earthworm, and Fish, as the input(s). EC G uses an existing state-of-the-art method bagged with Gaussian mixture model to label the dynamically changing behavioral data. The paper compares the results of the clustering ensemble used with baseline clustering methods on three behavior datasets as well as four benchmark datasets. The proposed sequenced (EC G F C ) method is finally compared on three classification error metrics: MSE, MAE, and SMAPE on behavioral and existing ensemble frameworks in the state-of-the-art.
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