Adaptive Deep Learning Techniques for Real-Time Shortest Path Optimization in Drone Ambulance Operations during Disaster

In disaster management, optimizing drone ambulance deployment for rapid and efficient response is paramount. This paper presents a novel deep learning algorithm designed to compute the shortest distance for drone ambulances navigating complex disaster scenarios. The approach incorporates advanced techniques, specifically Graph Neural Networks (GNNs) for dynamic route planning and Convolutional Neural Networks (CNNs) for feature extraction from geospatial data. The methodology begins with the collection and preprocessing of a diverse dataset, including geospatial information, disaster-related features, and drone specifications. This data is cleaned, normalized, and augmented to ensure comprehensive scenario coverage. A deep learning model is developed, integrating CNNs to process satellite images and GNNs to optimize routes through obstructed environments, with real-time data handling capabilities for predicting optimal paths by integrating both historical and current disaster conditions. During training, the data is split into training, validation, and test sets, with techniques such as back-propagation and reinforcement learning employed for real-time decision-making. The model’s performance is assessed using metrics such as accuracy, path length, and computational efficiency. After validation, the model is integrated into a drone control system to facilitate real-time distance calculations and dynamic route adjustments. This approach not only enhances the operational efficiency of drone ambulances but also establishes a robust framework for advancing disaster response technologies, demonstrating significant improvements in response times and the efficacy of drone-assisted medical interventions in critical situations.