## 1 Introduction
During the height of the pandemic in 2020, when Hurricane Sally made landfall and impacted the Florida Panhandle and Alabama, the State of Florida implemented non-congregate shelters in hotels and lodging facilities to provide safe shelter while preventing the spread of infection [15]. Non-congregate shelters offer a safer alternative by providing individual living spaces, for reducing disease transmission risk and improving overall shelter conditions [16]. However, the planning and allocation of these shelters require robust predictive models to ensure they meet the anticipated demand. Existing studies, such as those by [PERSON] et al. [17] and [PERSON] et al. [18] highlight the compound risks and decision-making complexities introduced by the pandemic during hurricane evacuations. Furthermore, large rural areas, often less equipped with emergency infrastructure, pose additional challenges that current models do not adequately address [19]. Existing literature lacks methods for accurate and real-time predictions of shelter demand, which are crucial to ensure that adequate resources are available and that shelters are neither overcrowded nor underutilized. Traditional models for predicting shelter demand often rely on historical data and static frameworks. For instance, logistic regression models [20] and agent-based simulations [21] have been widely used to estimate evacuation behaviors and shelter needs during hurricanes. However, these approaches often fail to capture the dynamic and evolving nature of disaster situations, especially under unique circumstances like the simultaneous occurrence of a pandemic and a high-intensity hurricane. Logistic regression assumes linear relationships and lacks the flexibility to capture non-linear, spatiotemporal dynamics. Agent-based simulations, while capable of simulating individual behaviors, often rely on predefined rules, making them less adaptable to real-time data and highly sensitive to initial assumptions. Both methods typically overlook real-time spatiotemporal data, limiting their predictive accuracy. Recent advancements in machine and deep learning have significantly enhanced disaster management, evacuation planning, and shelter management. Machine learning (ML) models, particularly those utilizing deep learning, metaheuristic optimization, and ensemble approaches, have gained prominence for their ability to capture complex, non-linear patterns in geohazard dynamics. In particular, [PERSON] and [PERSON] [22] and [PERSON] et al. [23] highlight deep learning and ensemble learning as powerful techniques for geohazard prediction. Furthermore, the studies including, [PERSON] et al. (2022) [24], [PERSON] et al. [25] and [PERSON] et al. [26], have demonstrated high predictive performance in landslide forecasting and geospatial risk analysis. While machine learning frameworks are very powerful for streamlining the tasks of feature selection, hyperparameter tuning, and optimization and provide high prediction accuracy, they often lack interpretability--an essential aspect for policymakers managing disaster response. These models often operate as \"black boxes,\" making it difficult to extract meaningful policy insights. By contrast, our proposed approach leverages Generalized Additive Models (GAM), offering greater interpretability of how key variables (e.g., school closures, stay-at-home orders) impact vehicle evacuation behavior, while providing ability to model complex spatial and temporal relationships. The GAM framework is specifically chosen for its efficacy in analyzing spatiotemporal data while allowing for a clear interpretation of how variables such as spatial location, day-of-week trends, and school closures impact vehicle movement. This interpretability is crucial for real-time decision-making and infrastructure planning, as we show in our study.