  Social networks can serve as a valuable communication channel for calls for
help, offering assistance, and coordinating rescue activities in disaster.
Social networks such as Twitter allow users to continuously update relevant
information, which is especially useful during a crisis, where the rapidly
changing conditions make it crucial to be able to access accurate information
promptly. Social media helps those directly affected to inform others of
conditions on the ground in real time and thus enables rescue workers to
coordinate their efforts more effectively, better meeting the survivors' need.
This paper presents a new sequence to sequence based framework for forecasting
people's needs during disasters using social media and weather data. It
consists of two Long Short-Term Memory (LSTM) models, one of which encodes
input sequences of weather information and the other plays as a conditional
decoder that decodes the encoded vector and forecasts the survivors' needs.
Case studies utilizing data collected during Hurricane Sandy in 2012, Hurricane
Harvey and Hurricane Irma in 2017 were analyzed and the results compared with
those obtained using a statistical language model n-gram and an LSTM generative
model. Our proposed sequence to sequence method forecast people's needs more
successfully than either of the other models. This new approach shows great
promise for enhancing disaster management activities such as evacuation
planning and commodity flow management.
