Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers

Papiya Debnath, Pankaj Chittora, Tulika Chakrabarti, Prasun Chakrabarti, Zbigniew Leonowicz, Michal Jasinski, Radomir Gono and Elżbieta Jasińska
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Papiya Debnath: Department of Basic Science and Humanities, Techno International New Town Rajarhat, Kolkata 700156, India
Pankaj Chittora: Department of Computer Science and Engineering, Techno India NJR Institute of Technology, Udaipur 313003, Rajasthan, India
Tulika Chakrabarti: Department of Basic Science, Sir Padampat Singhania University, Udaipur 313601, Rajasthan, India
Prasun Chakrabarti: Department of Computer Science and Engineering, Techno India NJR Institute of Technology, Udaipur 313003, Rajasthan, India
Zbigniew Leonowicz: Department of Electrical Engineering Fundamentals, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
Michal Jasinski: Department of Electrical Engineering Fundamentals, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
Radomir Gono: Department of Electrical Power Engineering, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic
Elżbieta Jasińska: Faculty of Law, Administration and Economics, University of Wroclaw, 50-145 Wroclaw, Poland

Sustainability, 2021, vol. 13, issue 2, 1-13

Abstract: 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.

Keywords: earthquake forecasting; supervised machine learning; classifiers (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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