Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction

Existing technologies employ different machine learning approaches to predict disasters from historical environmental data. However, for short-term disasters (e.g., earthquakes), historical data alone has a limited prediction capability. In this work, we consider social media as a supplementary source of knowledge in addition to historical environmental data. Further, we build a joint model that learns from disaster-related tweets and environmental data to improve prediction. We propose the combination of semantically-enriched word embedding to represent entities in tweets with their semantics representations computed with the traditional word2vec. Our experiments show that our proposed approach outperforms the accuracy of state-of-the-art models in disaster prediction.