Early detection of earthquake magnitude based on stacked ensemble model

Joshi, Anushka and Vishnu, Chalavadi and Mohan, C Krishna (2022) Early detection of earthquake magnitude based on stacked ensemble model. Journal of Asian Earth Sciences: X, 8. pp. 1-14. ISSN 2590-0560

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Abstract

A new machine learning model, named, EEWPEnsembleStack has been developed for predicting the magnitude of the earthquake from a few seconds of recorded ground motion after the arrival of the P phase. The testing and training dataset consists of 2360 and 591 strong-motion records from central Japan recorded by the Kyoshin Network. Eight parameters that are well correlated with the magnitude have been used for training and testing of the model. Feature ablation study using several models shows that a minimum mean absolute error of 0.42 has been obtained for the case when the model has been trained by using all parameters rather than by a single parameter. The model ablation study indicates that among all individually trained single models, the minimum error has been obtained for a Decision Tree regression model. However, the error is minimized when all machine learning models have been together utilized in the EEWPEnsembleStack model for the training purposes. The EEWPEnsembleStack model has been used to predict a 6.3 magnitude earthquake by using its 21 records from various stations that lie within 50 to 150 km epicentral distance. The predicted magnitude from the developed model using weighted magnitude prediction is obtained as 6.4, which is close to the actual magnitude. The comparison of the predicted magnitude of this earthquake from the developed model with that predicted by using popular τc and Pd methods clearly indicates the suitability of the developed machine learning model over other conventional models. © 2022

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IITH Creators:

IITH Creators	ORCiD
Mohan, C Krishna	UNSPECIFIED

Item Type:

Article

Additional Information:

Authors thankfully acknowledge National Research Institute for Earth Science and Disaster Resilience (NIED) for providing earthquake data. Authors are thankful to anonymous reviwers, Dr. Nuretdin Kaymakci (Associate editor) and Dr. Ibrahim Uysal (Co Editor in Chief) for their valuable suggestions for improvement of this manuscript. The data used in this research work is taken from National Research Institute for Earth Science and Disaster Resilience (NIED). Code availability (software application or custom code): Code used in this paper has been developed in Python. All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Anushka Joshi and Vishnu Chalavadi. The first draft of the manuscript was written by Anushka Joshi and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. The work presented in this paper does not involve research using humans and/or animals.

Uncontrolled Keywords:

Machine learning; Magnitude; Prediction; Strong motion

Subjects:

Computer science

Divisions:

Department of Computer Science & Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

28 Sep 2022 12:43

Last Modified:

28 Sep 2022 12:43