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.
@article{arxiv.2309.16747,
title = {Harnessing Diverse Data for Global Disaster Prediction: A Multimodal Framework},
author = {Gengyin Liu and Huaiyang Zhong},
journal= {arXiv preprint arXiv:2309.16747},
year = {2023}
}