Long-Short Term Memory (LSTM) Networks with Time Series and Spatio-Temporal Approaches Applied in Forecasting Earthquakes in the Philippines
| Autor: | Patrick Brian V. Arellano, Aleta C. Fabregas, Andrea Nicole D. Pinili |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Series (stratigraphy) Meteorology business.industry Computer science Spatiotemporal Analysis 02 engineering and technology Rule based algorithm computer.software_genre Field (geography) Physics::Geophysics Long short term memory 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Maximum magnitude Artificial intelligence Time series Geographic coordinate system business computer Natural language processing |
| Zdroj: | NLPIR |
| DOI: | 10.1145/3443279.3443288 |
| Popis: | A series of large earthquakes has been observed in different places in the Philippines in the year of 2019. These earthquake events led to destruction of infrastructures, households, heritage sites, and even multiple number of human lives. Earthquakes are hard to predict or forecast, which is why it is considered as a big challenge in the field of seismology. In this work, Rule Based Algorithm was used to classify the regions based on the latitude and longitude values, while Long Short-Term Memory (LSTM) Networks was used to forecast the following variables: frequency, maximum magnitude, and average depth of earthquake events in a specific region in a given year. The developed system was able to produce satisfactory results in the classification of regions, as well as in forecasting the maximum magnitude of earthquake events. The obtained results showed an improved prediction for the maximum magnitude, by considering both time series and spatiotemporal analysis, compared to previous prediction studies. |
| Databáze: | OpenAIRE |
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