Predicting the Magnitude of Earthquakes Using Grammatical Evolution
Throughout history, human societies have sought to explain natural phenomena through the lens of mythology. Earthquakes, as sudden and often devastating events, have inspired a range of symbolic and mythological interpretations across different civilizations. It was not until the 18th and 19th centu...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/18/7/405 |
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| Summary: | Throughout history, human societies have sought to explain natural phenomena through the lens of mythology. Earthquakes, as sudden and often devastating events, have inspired a range of symbolic and mythological interpretations across different civilizations. It was not until the 18th and 19th centuries that a more positivist and scientific approach began to emerge regarding the explanation of earthquakes, recognizing their origin as stemming from processes occurring beneath the Earth’s surface. A pivotal moment in the emergence of modern seismology was the Lisbon earthquake of 1755, which marked a significant shift towards scientific inquiry. This means that the question of how earthquakes occur has been resolved; thanks to advancements in scientific, geological, and geophysical research, it is now well understood that seismic events result from the collision and movement of lithospheric or tectonic plates. The contemporary challenge that emerges, however, lies in whether such seismic phenomena can be accurately predicted. In this paper, a systematic attempt is made to use techniques based on Grammatical Evolution to determine the magnitude of earthquakes. These techniques use freely available data in which the history of large earthquakes is introduced before the application of the proposed techniques. From the execution of the experiments, it has become clear that the use of these techniques can allow for more effective estimation of the magnitude of earthquakes compared to other machine learning techniques from the relevant literature. |
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| ISSN: | 1999-4893 |