Did They Sense it Coming? A Pipelined Approach for Tsunami Prediction Based on Aquatic Behavior Using Ensemble Clustering and Fuzzy Rule-Based Classification

Nikita Jain,Laura Garcia-Hernandez,Ajith Abraham,Lorenzo Salas-Morera,Deepali Virmani

doi:10.1109/access.2020.3022865

Abstract

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 design 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 (ECGFC) approach for designing a TWS, based on Ensemble Clustering (ECG) 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). ECG 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 (ECGFC) 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.

Highlights

The 2004 Indian Ocean tsunami, popularly known as ‘‘Boxing Day Tsunami’’ was marked as one of the most devastating events in the history of disaster science
In this paper, three behavioral datasets have been used to identify any pattern that can help in retrieving real-time alerts on seismic disturbances
A classification identifying alert or no-alert situations can be performed based on ambiguous behavioral signals exhibited by marine species in response to seismic perturbations

Summary

Introduction

The 2004 Indian Ocean tsunami, popularly known as ‘‘Boxing Day Tsunami’’ was marked as one of the most devastating events in the history of disaster science (because of high underwater seismic activity). Since repeated occurrences of tsunami have affected countries like. Srilanka, Japan, Thailand, and Indonesia, scientists and practitioners are taking cues from pre and post analysis of various tsunami events. This analysis has been presented in the form of analytical studies, algorithms, methods, simulations, and models describing the occurrence, prediction, or impact of such events. Different Tsunami Warning Systems (TWS) have been proposed, developed in research, and deployed to predict seismic perturbations. The stateof-the art has broadly classified these systems as Physiological (based on geophysics) [2]–[4], Societal (based on inter-human interaction), [5]–[7] and Nature-based

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