Large Earthquake Magnitude Prediction in Chile with Imbalanced Classifiers and Ensemble Learning
| Autor: | Manuel Jesús Fernández-Gómez, Alicia Troncoso, Francisco Martínez-Álvarez, Gualberto Asencio-Cortés |
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| Rok vydání: | 2017 |
| Předmět: |
Fluid Flow and Transfer Processes
Computer science business.industry Process Chemistry and Technology Social impact General Engineering Magnitude (mathematics) 02 engineering and technology Earthquake magnitude Machine learning computer.software_genre Ensemble learning imbalanced classification ensemble learning large earthquake prediction Field (computer science) Computer Science Applications 020204 information systems 0202 electrical engineering electronic engineering information engineering False positive paradox 020201 artificial intelligence & image processing General Materials Science Artificial intelligence Data mining business Instrumentation computer |
| Zdroj: | Applied Sciences; Volume 7; Issue 6; Pages: 625 |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app7060625 |
| Popis: | This work presents a novel methodology to predict large magnitude earthquakes with horizon of prediction of five days. For the first time, imbalanced classification techniques are applied in this field by attempting to deal with the infrequent occurrence of such events. So far, classical classifiers were not able to properly mine these kind of datasets and, for this reason, most of the methods reported in the literature were only focused on moderate magnitude prediction. As an additional step, outputs from different algorithms are combined by applying ensemble learning. Since false positives are quite undesirable in this field, due to the social impact that they might cause, ensembles have been designed in order to reduce these situations. The methodology has been tested on different cities of Chile, showing very promising results in terms of accuracy. |
| Databáze: | OpenAIRE |
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