Analysis of earthquake forecasting in India using supervised machine learning classifiers

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10247167" target="_blank" >RIV/61989100:27240/21:10247167 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2071-1050/13/2/971" target="_blank" >https://www.mdpi.com/2071-1050/13/2/971</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/su13020971" target="_blank" >10.3390/su13020971</a>

Result language

angličtina

Original language name

Analysis of earthquake forecasting in India using supervised machine learning classifiers

Original language description

Earthquakes are one of the most overwhelming types of natural hazards. As a result, successfully handling the situation they create is crucial. Due to earthquakes, many lives can be lost, alongside devastating impacts to the economy. The ability to forecast earthquakes is one of the biggest issues in geoscience. Machine learning technology can play a vital role in the field of geoscience for forecasting earthquakes. We aim to develop a method for forecasting the magnitude range of earthquakes using machine learning classifier algorithms. Three different ranges have been categorized: fatal earthquake; moderate earthquake; and mild earthquake. In order to distinguish between these classifications, seven different machine learning classifier algorithms have been used for building the model. To train the model, six different datasets of India and regions nearby to India have been used. The Bayes Net, Random Tree, Simple Logistic, Random Forest, Logistic Model Tree (LMT), ZeroR and Logistic Regression algorithms have been applied to each dataset. All of the models have been developed using the Weka tool and the results have been noted. It was observed that Simple Logistic and LMT classifiers performed well in each case. (C) 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20201 - Electrical and electronic engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Sustainability

ISSN

2071-1050

e-ISSN

—

Volume of the periodical

13

Issue of the periodical within the volume

2

Country of publishing house

CH - SWITZERLAND

Number of pages

13

Pages from-to

1-13

UT code for WoS article

000612058100001

EID of the result in the Scopus database

2-s2.0-85099551478