Recommendation of Emergency Response Plans for Meteorological Disasters Based on Deep Learning Models

Emergency response plan recommendation system's prompt and dynamic adjustment capabilities ensure the ultimate goal of ensuring people's life and property security. The thesis introduces three deep learning models for identifying emergency plans with high reference value. By model training and model parameter adjustments, relevant emergency departments can take prompt action under the guidance of the recommended plans for more efficient disaster responses. In the near future, the deep learning models proposed in the thesis can be applied to many other domains.