Deep spatio-temporal learning for multi-hazard events: A ConvGRU multi-label classification approach
Syeda Zoupash Zahra·Amjad Rehman·Kalim Sattar·Muhammad Faheem·Rab Nawaz Bashir·Najia Saher·Malik Muhammad Saad Missen
Abstract The forecasting of multi-hazards is a vital, though underinvestigated, area of disaster risk management. The traditional studies have mainly focused on single-hazard forecasting, thus leaving its utility in real-world and realistic scenarios. This study, in turn, presents a spatio-temporal multi-label classification model, a framework designed expressly to capture the complex interrelationships between a range of hazards. The methodological framework used disaster occurrence data from t