Presentation information

Oral presentation

General Session » [General Session] 2. Machine Learning

[2A1] [General Session] 2. Machine Learning

Wed. Jun 6, 2018 9:00 AM - 10:40 AM Room A (4F Emerald Hall)

座長:竹内 孝(NTTコミュニケーション科学基礎研究所)

9:20 AM - 9:40 AM

[2A1-02] Predicting Eruptions of Sakurajima by Stacked Recurrent Neural Network

〇Tsuyoshi Murata1, Hiep Le1, Masato Iguchi2 (1. Tokyo Institute of Technology, 2. Kyoto University)

Keywords:deep learning, volcanic eruption prediction, RNN

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%.