Predicting Eruptions of Sakurajima by Stacked Recurrent Neural Network
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- MURATA Tsuyoshi
- Tokyo Institute of Technology
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- LE Hiep
- Tokyo Institute of Technology
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- IGUCHI Masato
- Kyoto University
Bibliographic Information
- Other Title
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- Stacked Recurrent Neural Networkによる桜島噴火予測
Abstract
<p>Volcanic eruptions sometimes cause severe damage to many people. This paper explains our attempts for predicting volcanic eruptions from time series sensor data obtained from volcanic monitoring systems (strainmeters) located in Sakurajima. Given the time series data of strainmeters for 100 minutes, our goal is to predict future status of the volcano which is either "explosive" or "not explosive" for the 60 minutes immediately after the 100 minutes. We use stacked recurrent neural network for this task, and our method achieves 66.1% F-score on average. We also propose a four-stage warning system that classifies time series sensor data into the following categories: "Non-eruption", "May-eruption", "Warning" and "Critial". The percentage of "explosive" cases in "Critial" category is 51.9%.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2018 (0), 2A102-2A102, 2018
The Japanese Society for Artificial Intelligence
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Details 詳細情報について
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- CRID
- 1390001288047404800
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- NII Article ID
- 130007423841
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
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- Abstract License Flag
- Disallowed