The increasing complexity of multi-hazard disasters poses significant challenges to sustainable disaster risk governance. However, prevention and control measures are often scattered across heterogeneous and unstructured sources, limiting their systematic reuse and application. To address this issue, this study proposes a structured framework and data-driven analysis approach for organizing prevention and control measures in multi-hazard scenarios. By integrating multi-source information, a four-dimensional framework consisting of human, technical, engineering, and managerial measures was developed, together with a two-dimensional representation model incorporating disaster scenarios. Large language models (LLMs) were employed to automatically extract prevention and control measures from disaster-related documents and construct a multi-hazard prevention dataset. A case study of typhoon–hazardous chemical leakage scenarios yielded 1089 measurement records. Results show that managerial measures had the highest coverage (87.1%), while technical measures mainly focused on critical risk nodes such as leakage monitoring and automatic interlock control. Prevention and preparedness measures accounted for 67.4% of all records, reflecting a proactive risk-governance orientation. Strong associations were observed among the four categories of measures (Jaccard coefficient: 0.624–0.879). The proposed framework supports the structured representation, knowledge organization, and data-driven analysis of prevention and control measures, providing a foundation for sustainable disaster risk governance and resilient emergency management.
Abstract. Natural and technological disasters continue to threaten communities, infrastructure, and the environment, and the cascading nature of contemporary risks complicates their assessment. This study presents a disaster risk analysis model coupling Geographic Information Systems (GIS), ensemble machine learning, and AI-driven interpretation tools, collectively termed GeoAI, to support multi-hazard risk assessment, resilience planning, and citizen-oriented risk communication. Rather than building isolated models per hazard, the framework applies a single, modular pipeline consistently across flood, wildfire, earthquake, landslide, drought, and urban heat island hazards. Hazard, vulnerability, and exposure layers are derived from open geospatial datasets and processed in QGIS, after which Random Forest and XGBoost classifiers generate hazard and vulnerability maps validated using AUC-ROC, F1-score, and Cohen's Kappa. A distinctive component is an AI agent built on Large Language Models (LLMs) and the Model Context Protocol (MCP), which queries structured risk databases and produces region-specific narrative risk reports in natural language. Outputs are delivered through a web-based platform with an MCP-connected chatbot, lowering the barrier to understanding complex risk information for experts and the public. The model was tested in Türkiye's Marmara Region, which is important due to its dense population, heavy industry, and earthquake risk along the North Anatolian Fault. The pilot showed good results: XGBoost performed better than the baseline models, and the LLM-based interpretation layer gave clear, well-grounded outputs. The framework offers a scalable, open, interoperable approach linking spatial analytics, machine learning, and generative AI for evidence-based disaster risk reduction.
Unknown authors· The International Archives o...· 0 citations
Disaster Risk Reduction (DRR) has emerged as a critical global priority in response to the increasing frequency, intensity, and complexity of natural and human-induced disasters. Climate change, rapid urbanization, environmental degradation, and socio-economic inequalities have significantly amplified disaster risks, particularly in developing and vulnerable regions. This paper presents a comprehensive multidisciplinary analysis of disaster risk reduction strategies by integrating perspectives from engineering, environmental science, social sciences, economics, public policy, and information technology. The study emphasizes that effective DRR cannot be achieved through single-discipline interventions but requires a holistic framework that combines structural and non-structural measures, community participation, governance mechanisms, and technological innovation. The paper systematically reviews existing DRR frameworks and international agreements, including the Sendai Framework for Disaster Risk Reduction, highlighting their strengths and limitations. A detailed literature survey examines recent research on hazard assessment, vulnerability analysis, resilience building, early warning systems, and post-disaster recovery. The proposed methodology adopts a systems-based approach, integrating qualitative and quantitative methods such as risk modeling, stakeholder analysis, and multi-criteria decision-making. Key performance indicators are used to evaluate the effectiveness of DRR strategies across different hazard contexts. Results indicate that multidisciplinary DRR strategies significantly enhance preparedness, reduce disaster losses, and improve recovery outcomes when compared to sector-specific approaches. The discussion underscores the importance of adaptive governance, data-driven decision-making, and inclusive community engagement. The paper concludes by proposing a scalable and adaptable DRR framework suitable for policymakers, practitioners, and researchers, contributing to sustainable development and resilience-building efforts worldwide.
Rajesh Sharma· International Journal of Eme...· 0 citations
The Italian civil protection system is a comprehensive multi-level framework aimed at protecting people, property and the environment from natural and anthropogenic hazards. Hazard and risk models are essential for all phases of risk management. They guide preparedness and prevention policies, such as plans and structural retrofit programmes. They also underpin emergency management through early warning systems, real time monitoring, rapid damage assessments, and probabilistic models that support civil protection decision-making. In recovery phases, they inform reconstruction priorities and enable “build back better” strategies. An increasingly important role is played by multi-risk approaches. Risk perception and communication, as well as the emerging challenges posed by climate change, deserve special attention. Future priorities also include improved multi-risk assessment, integration of social vulnerability, and the use of artificial intelligence in hazard and risk modelling to enhance evidence-based decisions in disaster risk management.
D. Di Bucci· Techne : Journal of Technolo...· 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
Typhoons often cause severe casualties, property losses, and infrastructure damage, and high-resolution spatial risk assessment is an important basis for developing effective disaster prevention and mitigation strategies. However, most existing typhoon risk assessments are conducted at relatively coarse spatial scales and provide limited representation of intra-urban differences in mitigation capacity. To address this gap, this study takes Haikou, China, as the study area and develops a spatially detailed mitigation-capacity indicator system. Mitigation capacity is incorporated as a key dimension into the conventional hazard–exposure–vulnerability framework. Based on multi-source geospatial data, data mining, and spatial analysis, a risk assessment system comprising 23 indicators was established. Indicator weights were determined using the analytic hierarchy process, and all indicator layers were harmonized to generate a typhoon risk map on a 30 m analytical grid. The results show that the high-resolution risk maps can effectively characterize the spatial extent and level differentiation of typhoon risk while revealing significant spatial heterogeneity in risk at the fine grid scale. In Haikou, 18.99% of the area is classified as being at high and very high risk levels, mainly distributed along the coastal zones of Shishan Town, Xixiu Town, Changliu Town, Lingshan Town, and Yanfeng Town. A preliminary plausibility check was conducted using eight georeferenced typhoon-related fatality locations recorded from 2015 to 2024. Five were located in high- or very-high-risk zones. Given the limited sample size, this comparison does not constitute formal statistical validation, but the observed spatial correspondence provides preliminary support for the plausibility of the assessment results. This study provides spatially explicit decision support for identifying intra-urban variations in typhoon risk, delineating priority areas for disaster mitigation, and optimizing the allocation of mitigation resources.
Rapid urbanization and escalating climate-related risks have significantly heightened the disaster vulnerability of cities worldwide. Traditional urban planning methods, constrained by their reliance on static historical data and reactive strategies, are increasingly inadequate for addressing contemporary hazard complexities. Artificial Intelligence (AI) presents a transformative opportunity for disaster-resilient urban planning through data-driven insights and dynamic decision-making capabilities. This paper evaluates the role of AI in enhancing urban resilience and proposes a conceptual framework grounded in a systematic review of peer reviewed literature and comparative global case studies. The study employs a qualitative review methodology encompassing an examination of academic publications, policy reports, and case studies from disaster prone metropolitan areas. Secondary data were sourced from Scopus, Web of Science, institutional databases, and publicly accessible urban and climate datasets. Results indicate that AI-enabled approaches demonstrate superior capabilities in risk recognition, integrated data analytics, and the formulation of proactive, adaptive planning strategies compared to conventional methods. Comparative case study analysis of Chennai, Rotterdam, Tokyo, and Singapore reveals that the efficacy of AI applications is contingent upon data availability, technological infrastructure, and institutional governance capacity. The study concludes that while AI holds substantial potential to transform disaster-resilient urban design, its effective implementation necessitates robust institutional frameworks, ethical governance, and context-specific adaptation, particularly in developing regions.
Senthil M· 2026 11th International Conf...· 0 citations