# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
## 3 Methodology
### Development of Predictive Models for Emergency Resource Allocation
The training and hyperparameter tuning were performed on a high-performance computing environment equipped with an Intel Xeon Gold 6248R 3.00 GHz CPU, 256 GB of RAM (Intel Corporation, Santa Clara, CA, USA), and an NVIDIA A100 GPU (40 GB) (NVIDIA Corporation, Santa Clara, CA, USA). While most operations were executed on the CPU, GPU acceleration was selectively utilized for large-scale matrix computations and hyperparameter optimization. This setup ensured sufficient computational capacity to handle spatiotemporally complex and high-volume datasets and to support the repeated training of ensemble-based models with high stability. Two separate target variables were modeled. The first was the optimal number of ambulances per EMS center, which serves as the foundation for demand-based resource allocation strategies. The second was the dispatch duration from scene to hospital, which is critical for evaluating emergency response performance in relation to geographic and traffic conditions. These outputs were subsequently used as core inputs in the simulation phase for generating region-specific weight factors and conducting scenario-based policy analyses. The dependent variable for the first predictive model--namely, the optimal number of ambulances per EMS center--was defined based on empirical data from 100 EMS centers identified by the National Fire Agency as having achieved an appropriate level of deployment. This determination was made by comprehensively considering factors such as operational efficiency, responsiveness to local demand, population characteristics, dispatch frequency, response time, hospital accessibility, and environmental conditions. These centers were evaluated as benchmark cases where such variables were well-balanced and were therefore designated as a reference baseline within the training dataset. The selection criteria were established in consultation with policy officers and field-level administratorsat the NFA to ensure both operational validity and policy relevance. Using this group of 100 reference centers, the model was trained to predict appropriate deployment levels for other regions by comparing their respective combinations of input features. This similarity-based approach enabled the model to account for structural and contextual heterogeneity across EMS centers and quantify relative deployment needs in a more nuanced and context-sensitive manner. This modeling approach thus departs from conventional methods that rely solely on historical dispatch volumes and offers a more rigorous and data-driven foundation for resource planning. In conclusion, the modeling strategy adopted in this study integrates multiple strengths: objective feature selection using Lasso regression, structural diversity through ensemble tree-based algorithms, performance optimization via cross-validation and hyperparameter tuning, and practical applicability by balancing predictive accuracy and interpretability. This framework is designed not only to enhance short-term operational efficiency but also to serve as a robust quantitative foundation for long-term emergency medical service policy planning and infrastructure investment decisions.