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

Existing technologies employ different machine learning approachesto predict disasters from historical environmental data. However,for short-term disasters (e.g., earthquakes), historical data alonehas a limited prediction capability. In this work, we consider so-cial media as a supplementary source of knowledge in additionto historical environmental data. Further, we build a joint modelthat learns from disaster-related tweets and environmental data toimprove prediction. We propose the combination of semantically-enriched word embedding to represent entities in tweets with theirsemantics representations computed with the traditionalword2vec.Our experiments show that our proposed approach outperformsthe accuracy of state-of-the-art models in disaster prediction