Can Eruptions Be Predicted? Short-Term Prediction of Volcanic Eruptions via Attention-Based Long Short-Term Memory

Abstract

Short-term prediction of volcanic eruptions is one of the ultimate objectives of volcanology. At Sakurajima volcano, an active volcano in Japan, experts monitor the volcanic sensor data and analyze the prior signal to predict the eruptions. Even though experts derived some patterns, it is hard to make a good prediction due to handcrafted features. To address this issue, we propose to predict eruptions using machine learning. In this paper, we attempt to predict the eruptions hourly by adapting several machine learning methods including traditional and deep learning approaches. As recurrent neural network is well-known for extracting the time-sensitive features, we propose the model especially for volcanic eruption prediction named VepNet. The assumption is based on domain knowledge that some specific triggers are the main causes of future eruptions. To take this advantage, VepNet deploys an attention layer to locate and prioritize these triggers in decision making. The extensive experiments ever conducted using data from Sakurajima volcano showed the effectiveness of deep learning approach over the traditional approach. On top of that, VepNet showed its effectiveness on prediction with AUC-score up to 0.8665. Moreover, an attempt has been made to explain the mechanism of the eruptions by analyzing the attention layer of VepNet. Lastly, to support volcano expert in issuing warnings and the safety of living people around Sakurajima, a warning system named 3LWS is proposed. The system predicted the eruptions hourly with high accuracy and reliability with the eruption rate up to 68.97% in the High-Risk level.