Predicting Eruptions of Sakurajima by Stacked Recurrent Neural Network

Abstract

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%.