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

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 (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.

A Fuzzy-Logic Approach for Optimized and Cost-Effective Early Warning System for Tsunami Detection

“…Recent research shows that tsunami warning systems can be classified based on computation or mathematic calculations. Nature-based systems use ecology, such as unexpected behavior and response of animals or plants to the changes in water due to seismic effects [19]. The authors used datasets of different marine animals and the ensemble clustering technique to categorize this strange behavior.…”

NatDisP – An Intelligent Natural Disaster Predictor

“…A Pipelined Approach for Tsunami Prediction Based on Aquatic Behavior Using Ensemble Clustering and Fuzzy Rule-Based Classification",have given that, out of the dreadful disasters that are prevalent in the world Tsunami is one of them and early and effective warning system for tsunamis is there intermediate goal seismic data collected from under the water can be of great help in the process of predicting tsunamis but in many cases these warning systems fail and are not able to give alerts early enough for performing the required evacuation so that is the reason they in their paper have proposed an approach for designing a tws which sequenced and it is based on ECG i. e ensemble classification and clustering this is also done by taking the behavioral data sets of three aquatic animals fishes earthworms and turtles this will be the input and they use already existing methods. [10].…”

“…The need for developing an effective and early system of warning for tsunami is very important, researchers across the world are working for it. Seismic water from under the water got by different methods can be used here in order to develop a warning system for tsunamis [24]. Here using machine learning and fuzzy algorithms to predict the tsunamis and plan well in advance for it.…”