Harnessing Diverse Data for Global Disaster Prediction: A Multimodal
  Framework
Gengyin Liu Huaiyang Zhong
As climate change intensifies, the urgency for accurate global-scale disaster
predictions grows. This research presents a novel multimodal disaster
prediction framework, combining weather statistics, satellite imagery, and
textual insights. We particularly focus on "flood" and "landslide" predictions,
given their ties to meteorological and topographical factors. The model is
meticulously crafted based on the available data and we also implement
strategies to address class imbalance. While our findings suggest that
integrating multiple data sources can bolster model performance, the extent of
enhancement differs based on the specific nature of each disaster and their
unique underlying causes.