  Disaster prediction is one of the most critical tasks towards disaster
surveillance and preparedness. Existing technologies employ different machine
learning approaches to predict incoming disasters from historical environmental
data. However, for short-term disasters (e.g., earthquakes), historical data
alone has a limited prediction capability. Therefore, additional sources of
warnings are required for accurate prediction. We consider social media as a
supplementary source of knowledge in addition to historical environmental data.
However, social media posts (e.g., tweets) is very informal and contains only
limited content. To alleviate these limitations, we propose the combination of
semantically-enriched word embedding models to represent entities in tweets
with their semantic representations computed with the traditionalword2vec.
Moreover, we study how the correlation between social media posts and typhoons
magnitudes (also called intensities)-in terms of volume and sentiments of
tweets-. Based on these insights, we propose an end-to-end based framework that
learns from disaster-related tweets and environmental data to improve typhoon
intensity prediction. This paper is an extension of our work originally
published in K-CAP 2019 [32]. We extended this paper by building our framework
with state-of-the-art deep neural models, up-dated our dataset with new
typhoons and their tweets to-date and benchmark our approach against recent
baselines in disaster prediction. Our experimental results show that our
approach outperforms the accuracy of the state-of-the-art baselines in terms of
F1-score with (CNN by12.1%and BiLSTM by3.1%) improvement compared with last
experiments
