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A platform for research: civil engineering, architecture and urbanism
An attention-based LSTM network for large earthquake prediction
https://orcid.org/0000-0002-4388-100X
https://orcid.org/0000-0002-2355-4204
Abstract Due to the complexity of earthquakes, predicting their magnitude, timing and location is a challenging task because earthquakes do not show a specific pattern, which can lead to inaccurate predictions. But, using Artificial Intelligence-based models, they have been able to provide promising results. However, few mature studies are dealing with large earthquake prediction, especially as a regression problem. For these reasons, this paper investigates an attention-based LSTM network for predicting the time, magnitude, and location of an impending large earthquake. LSTMs are used to learn temporal relationships, and the attention mechanism extracts important patterns and information from input features. The Japan earthquake dataset from 1900 to October 2021 was used because it represents a highly seismically active region known for its large earthquakes. The results are examined using the metrics of MSE, RMSE, MAE, R-squared, and accuracy. The performance results of our proposed model are significantly better compared to other empirical scenarios and a selected baseline method, where we found that the MSE of our model is better by approximately 60%.
Highlights An attention-based LSTM network for earthquake prediction. Clustering seismic regions for location prediction. Time, magnitude, and location of earthquakes of magnitude >6 are predicted. Two different seismic regions are studied: Japan and the northern Red Sea. Discussion and comparison of results.
An attention-based LSTM network for large earthquake prediction
https://orcid.org/0000-0002-4388-100X
https://orcid.org/0000-0002-2355-4204
Abstract Due to the complexity of earthquakes, predicting their magnitude, timing and location is a challenging task because earthquakes do not show a specific pattern, which can lead to inaccurate predictions. But, using Artificial Intelligence-based models, they have been able to provide promising results. However, few mature studies are dealing with large earthquake prediction, especially as a regression problem. For these reasons, this paper investigates an attention-based LSTM network for predicting the time, magnitude, and location of an impending large earthquake. LSTMs are used to learn temporal relationships, and the attention mechanism extracts important patterns and information from input features. The Japan earthquake dataset from 1900 to October 2021 was used because it represents a highly seismically active region known for its large earthquakes. The results are examined using the metrics of MSE, RMSE, MAE, R-squared, and accuracy. The performance results of our proposed model are significantly better compared to other empirical scenarios and a selected baseline method, where we found that the MSE of our model is better by approximately 60%.
Highlights An attention-based LSTM network for earthquake prediction. Clustering seismic regions for location prediction. Time, magnitude, and location of earthquakes of magnitude >6 are predicted. Two different seismic regions are studied: Japan and the northern Red Sea. Discussion and comparison of results.
An attention-based LSTM network for large earthquake prediction
https://orcid.org/0000-0002-4388-100X
https://orcid.org/0000-0002-2355-4204
Abstract Due to the complexity of earthquakes, predicting their magnitude, timing and location is a challenging task because earthquakes do not show a specific pattern, which can lead to inaccurate predictions. But, using Artificial Intelligence-based models, they have been able to provide promising results. However, few mature studies are dealing with large earthquake prediction, especially as a regression problem. For these reasons, this paper investigates an attention-based LSTM network for predicting the time, magnitude, and location of an impending large earthquake. LSTMs are used to learn temporal relationships, and the attention mechanism extracts important patterns and information from input features. The Japan earthquake dataset from 1900 to October 2021 was used because it represents a highly seismically active region known for its large earthquakes. The results are examined using the metrics of MSE, RMSE, MAE, R-squared, and accuracy. The performance results of our proposed model are significantly better compared to other empirical scenarios and a selected baseline method, where we found that the MSE of our model is better by approximately 60%.
Highlights An attention-based LSTM network for earthquake prediction. Clustering seismic regions for location prediction. Time, magnitude, and location of earthquakes of magnitude >6 are predicted. Two different seismic regions are studied: Japan and the northern Red Sea. Discussion and comparison of results.
An attention-based LSTM network for large earthquake prediction
Berhich, Asmae (author) / Belouadha, Fatima-Zahra (author) / Kabbaj, Mohammed Issam (author)
2022-11-12
Article (Journal)
Electronic Resource
English
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