Jun 2026· International Journal of Engineering and Geosciences· Vol 11, pp. 597-613· 0 citations· 21 references
TL;DR
A context-aware SAR framework was developed in this study to identify and estimate key parameters in rescue operations, activities, and environmental conditions, and to model the problem within a spatial information system, by integrating ant colony optimization (ACO) with the SARSA reinforcement learning algorithm.
Abstract
Natural hazards such as earthquakes can cause significant loss of life and extensive damage worldwide each year. Search and Rescue (SAR) operations in such critical situations require an urgent need for speed and efficiency, but are also complex challenges, given their dynamic and uncertain nature. This challenge requires approaches that integrate realistic environmental modeling with intelligent decision-making. Geospatial information systems are a powerful tool that can provide robust representations of complex terrains under time-critical circumstances, and artificial intelligence techniques can simplify decision-making and optimize decision-making under time-sensitive circumstances. In addition, context-aware SAR enables the precise identification and quantification of situational parameters, allowing better tailoring of operations to the real world. A context-aware SAR framework was developed in this study to identify and estimate key parameters in rescue operations, activities, and environmental conditions, and to model the problem within a spatial information system. And by integrating ant colony optimization (ACO), which is well-suited to modeling cooperative search behaviors, with the SARSA reinforcement learning algorithm, which is well-suited to discrete decision-making environments, a hybrid method was proposed. As part of this integration, a pheromone-like reinforcement mechanism was utilized to enhance overall performance and adaptability. A comparison of this hybrid approach against a standalone ACO model revealed that it outperformed it in both operational efficiency and solution quality, underscoring its potential for real-world SAR applications.
In the critical domain of emergency response, rapid building evacuation and rescue are often hindered by complex layouts and dynamic hazards such as fire spread, toxic smoke, structural instability, and unpredictable occupant behaviour. To address the challenge of optimizing rescue sweep strategies, this paper establishes a comprehensive mathematical framework that transitions from traditional static planning to dynamic, adaptive decision-making, with the objectives of minimizing rescue completion time and maximizing occupant safety under uncertain conditions. First, a three-level evaluation system based on the AHP–Entropy Weight Method is constructed to quantify 32 critical influence factors, ranging from environmental conditions and building layout to occupant behaviour and technical support. The resulting weighted framework provides a data-driven foundation for subsequent optimization models. Three representative building configurations are then examined. For a 1D linear layout, a Divide-and-Conquer strategy reduces completion time by 42% compared with a single-entry approach. For a 2D branching layout, the rescue problem is formulated as a load-balanced Multi-Agent Traveling Salesman Problem (MA-TSP). For a 3D high-rise structure, a hierarchical Pincer Strategy is developed by nesting the MA-TSP within a multi-objective vehicle-routing framework and strategically leveraging vertical assets such as fire-service elevators. Finally, a Dynamic Adaptive Replanning Model is introduced using time-dependent hazard functions, dynamic edge costs, and occupant re-entry events. A Rolling-Horizon Replanning mechanism continuously updates rescue routes as the emergency evolves.
Qihao Chen· Journal of Frontiers in Tech...· 0 citations
Natural disasters frequently inflict severe damage to the built environment, which demands a rapid, reliable, and cost-effective damage assessment for emergency response. However, traditional methods for post-disaster damage assessment often rely on static, labor-intensive data collection strategies that can be prohibitively expensive and struggle to adapt to dynamic post-disaster conditions. In this study, we propose a cost-aware Bayesian optimization framework combined with level-set estimation that continuously guides autonomous data collectors, e.g., an unmanned aerial vehicle (UAV), toward the most informative regions. By dynamically updating damage estimates across different geographic zones, our approach systematically reduces uncertainty while minimizing operational costs. The proposed framework is first validated using a controlled synthetic toy study, demonstrating the agent's ability to efficiently trace damage boundaries, recover the underlying damage map, and rapidly reduce predictive uncertainty. Furthermore, the approach is evaluated using high-fidelity disaster data generated by the Regional Resilience Determination (R2D) software. The results of the algorithm provide accurate and timely damage estimates that support informative and fast emergency response.
Boyang Xu, M. R. Gahrooei, Mohammad Ilbeigi et al.· 0 citations
Environmental disasters, particularly earthquakes and floods, present immense challenges to humanity, demanding effective assessment and comprehensive management strategies. Given the increasing frequency and intensity of such events, their devastating impact on lives, the environment, and infrastructure calls for innovative approaches. Earthquakes can devastate entire urban areas within seconds, while flood events can also have long-term effects on the environment and livelihoods. Conventional disaster management methodologies often result in inefficient use of resources and suboptimal decision-making due to inadequate, near real-time, and cost-effective damage assessment capabilities. To address this critical gap, this paper introduces PostDisaster, an innovative platform designed to enhance disaster management and assessment and strengthen the resilience of the affected regions through the deployment of Virtual Reality (VR), Mixed Reality (MR), AI-supported satellite analysis, and drone-based tools. To overcome current technological limitations, the PostDisaster system provides decision-makers with tools for action planning and detailed damage analysis, delivering critical data at varying levels of detail. An AI-supported satellite image analysis tool delivers an immediate, macroscopic overview of the disaster’s scope. To obtain a more accurate overview of the situation, a drone-based tool generates a highly detailed 3D reconstruction of the affected area. which can be viewed in VR to enable decision-makers remote assessment of the situation and planning of measures. Finally, an on-site MR application allows users to walk through damaged infrastructure, mark damages and reconstruct the infrastructure. The reconstructed infrastructure with the marked damages can be subsequently viewed as digital representation in VR, ensuring comprehensive traceability of decisions for e.g., mission evaluations, debriefings or insurance assessments. This paper describes the current state of the PostDisaster system and the outcomes of the requirements analysis phase.
Elisabeth Broneder, Stefan Penn, Lukas Hofstetter et al.· AHFE International· 0 citations
A critical gap in disaster response persists between optimized logistical plans and the complex, often unpredictable evacuation behaviors of affected populations. Traditional location-allocation models often prove insufficient in practice, as they typically treat evacuees as homogeneous, rational actors, thereby overlooking the dynamic, hierarchical needs that govern decision-making on the ground. This study addresses this gap by introducing a hybrid optimization framework that systematically translates data-driven behavioral insights into a multi-objective model for shelter location and material assignment. Rather than modeling microscopic routing behaviors, our methodological innovation lies in a two-stage pipeline. First, we integrate thematic analysis of large-scale social media data (3400 relevant posts) from recent earthquakes to extract and quantify time-varying demand patterns for three core needs: essential survival, medical treatment, and psychological needs. Second, these empirically derived priority dynamics are embedded as dynamic parameters within the optimization model. Validated against the 2013 Ya'an earthquake case study and compared with classical baselines (e.g., the p-median model), the framework reduces the average evacuation distance while ensuring feasible access for heterogeneous vulnerable groups. Concurrently, it enhances resource equity by dynamically prioritizing material allocation based on the evolving urgency of evacuee needs. This research thus contributes not only a specific decision-support tool but also a generalizable paradigm for bridging descriptive behavioral data with prescriptive operations models, offering a more realistic foundation for risk analysis in humanitarian logistics.
The assignment of a limited number of search and rescue (SAR) personnel to multiple, geographically dispersed disaster sites is a critical decision problem that directly determines the effectiveness of the initial response. Although this problem extends the classical assignment problem, the multidimensional nature of disaster operations cannot be adequately captured by single criterion distance minimization. In this study, the problem is modeled around a unified objective function (Φ) that integrates personnel competence, travel proximity, disaster demand, coverage ratio, and operational team cohesion. Under this common objective, Mixed Integer Linear Programming (LP/MILP) and four nature-inspired metaheuristics (Grey Wolf Optimizer, Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization) are evaluated within a fair comparison framework. The method's dynamic incremental data mechanism also allows for the addition of reinforcement personnel arriving after the initial assignment and newly reported crash areas, while previously applied assignments remain locked. The method is validated on an urban earthquake scenario for the Çukurova district of Adana province, inspired by the 2023 Kahramanmaraş earthquakes. Across four scenarios representing a gradual transition from initial response to full capacity containing three disaster types, a total of 600 runs are evaluated using descriptive statistics, non-parametric hypothesis tests (the Friedman test and the Nemenyi post-hoc test), and convergence and sensitivity analyses. The principal finding is that the incremental solution (Φ = 0.8471) yields a higher objective value than the static approach solving the same data in a single pass (Φ = 0.8255), showing that the locked field state preserves operational continuity without sacrificing solution quality.
Nurettin Havutçu, Mevlüt Ersoy· Advances in Artificial Intel...· 0 citations
This paper presents a modular decision support system that infers the primary location of the user or the reported incident and a situation-aware risk level from multi-turn Turkish disaster dialogues between a help-seeking user and an AI-supported emergency assistant. The assistant guides the user with follow-up questions about health status, number of affected people, structural damage, environmental hazards, and known nearby landmarks to complete missing information. The system manages the dialogue with a finite state machine, determines the location by linking user cues to a local GeoJSON point-of-interest database and by landmark verification, and produces explainable risk scores with Multi-Criteria Decision Analysis. The key novelty is treating landmarks as an evidence layer that verifies the current location hypothesis through proximity and clustering instead of directly replacing candidates based on a landmark signal. In a ten-scenario pilot evaluation, accuracy, end-to-end latency, and token usage are reported for three configurations.
Eren Varlıker, Yusuf Sinan Özmen, Selim Balcisoy· Signal Processing and Commun...· 0 citations