Decision-Making Model Construction of Emergency Material Allocation for Critical Incidents Based on BP Neural Network Algorithm: An Overview

Abstract

Effective emergency material allocation is critical for mitigating the impact of critical incidents. This paper proposes a decision-making model for emergency material allocation based on the Backpropagation (BP) Neural Network algorithm. The model is designed to learn from historical emergency incidents and optimize resource allocation in real-time. The study includes a comprehensive case study, comparing the performance of the BP Neural Network model with traditional allocation methods. Results indicate superior response times, resource utilization efficiency, and overall effectiveness of the BP Neural Network model. Challenges and limitations in implementing the model are discussed, and recommendations for future research, including algorithm exploration and real-time adaptability enhancements, are presented. This research contributes to the advancement of intelligent decision-making models for emergency management.