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| Μορφή: | Recurso digital |
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Zenodo
2025
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| Θέματα: | |
| Διαθέσιμο Online: | https://doi.org/10.5281/zenodo.16880952 |
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Πίνακας περιεχομένων:
- <p><span>Earthquakes are among the most severe natural disasters, causing enormous destruction. Despite geologists trying a variety of methodologies to predict the likelihood of an earthquake striking a specific location, studies have yielded no solid results. The capacity to anticipate the depth of an earthquake allows individuals to better prepare for and be aware of potential hazards. Several machine learning techniques can estimate the depth of an earthquake. To get the best results, you should consider multiple ways. The proposed technique employs seismic data to train a random forest regression model capable of predicting earthquake depths. Root mean square error (RMSE), root mean square error (MSE), and R2 score are some of the metrics used to assess the success of the proposed method. The approach accurately predicts the earthquake's depth at a range of future locations.</span></p>