A Multi-Source Data-Driven Natural Disaster Emergency Management System: Risk Early Warning Framework, Social Media Opinion Mining, and Monitoring System Development
Jul 2026· Journal of Intelligent Decision Making and Granular Computing· Vol 2, pp. 187-200· 0 citations· 19 references
TL;DR
A natural disaster risk early warning system framework that spans the entire cycle of pre-disaster prevention, mid-disaster emergency response, and post-disaster recovery is constructed, forming a closed-loop governance chain of “monitoring–assessment–release–response–feedback–optimization.”
Abstract
In response to the systemic lag and reactive difficulties present in traditional natural disaster management models regarding the timeliness of risk perception, the accuracy of decision-making, and the coordination of emergency responses, this paper constructs a natural disaster risk early warning system framework that spans the entire cycle of pre-disaster prevention, mid-disaster emergency response, and post-disaster recovery, forming a closed-loop governance chain of “monitoring–assessment–release–response–feedback–optimization.” On this basis, taking the “8·21” wildfire in Beibei, Chongqing, China, in 2022 as a typical case, a social media public opinion visualization analysis was conducted based on 5,507 text posts from the Weibo platform. Through word frequency statistics, temporal comment volume analysis, and word cloud visualization, the study reveals the spatially structured characteristics of public attention and the temporal evolution pattern of sentiment tendencies. Finally, from four dimensions—improving classified monitoring and graded early warning systems, establishing information sharing and hierarchical reporting systems, perfecting early warning release and graded response mechanisms, and advancing the informatization of monitoring, forecasting, and early warning efforts—the paper proposes a development path for a comprehensive natural disaster monitoring and early warning system, aiming to provide theoretical references and practical guidance for advancing the modernization of natural disaster prevention and emergency management capabilities.
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
Natural and man-made disasters continue to extract a catastrophic toll on human lives, infrastructure, and economies across the world. Between 2000 and 2022, disaster events claimed over 1.9 million lives and caused economic losses exceeding USD 2.97 trillion, with climate change accelerating the frequency and intensity of hydro-meteorological events such as cyclones, floods, and droughts. In India, a country that ranks among the world's most disaster-prone nations, the lack of an integrated, technology-enabled disaster response coordination system has repeatedly resulted in delayed relief operations, duplicated resource deployment, and inadequate victim tracing. This paper proposes a comprehensive Disaster Response and Emergency Management System (DREMS) — an AI-driven, IoT-integrated, multi-agency coordination platform designed to support all four phases of disaster management: mitigation, preparedness, response, and recovery. The DREMS architecture comprises six functional modules: a multi-hazard early warning subsystem using satellite and ground sensor fusion, an AI-based damage and casualty assessment engine using satellite imagery analysis, a real-time resource allocation and dispatch optimizer, a decentralized mesh-network communication infrastructure for connectivity-denied disaster zones, a victim registration and family reunification portal, and a post-disaster recovery tracking dashboard. The system was evaluated through a simulated large-scale flood disaster scenario modelled on the 2015 Chennai floods, demonstrating a 34% reduction in resource dispatch response time, 91.4% accuracy in satellite-based damage classification, 87.6% victim registration coverage in simulation, and communication continuity in connectivity-denied zones with mesh network node density of 1 node per 0.8 km². The proposed DREMS framework offers a deployable, scalable model for integrated disaster management aligned with India's National Disaster Management Authority guidelines.
Keywords — Disaster Management, Emergency Response, Early Warning System, Satellite Imagery, AI Damage Assessment, Mesh Network, Resource Allocation, NDMA, IoT, Flood Response
Agesta Jenifer A Agesta Jenifer A, Saran S Saran S, Potrivel K Potrivel K et al.· International Scientific Jou...· 0 citations
Natural disasters with tremendous impacts on human lives, infrastructure, and ecosystems are frequent all over the world, which calls for intelligent, data-driven decision support systems for early diagnosis and effective crisis management. This paper demonstrates a Natural Disaster Diagnosis and Crisis Management System design and development featuring real-time environmental sensing, technological monitoring, and pre-emptive response planning within a unified decision-support framework. The proposed system include predict disasters using machine learning. It follows a modular architecture that integrates analytical risk assessment, formulation of preventive strategy, and dynamic action planning through an interactive GUI. NDDCMS structures disaster management into five operational phases, namely disaster diagnosis, early warning indicators, response during the disaster, post-disaster recovery, and preventive planning. Each phase integrates stepwise risk indicators and decision inputs to support officials and community response teams. It relies on quantifiable environmental parameters (e.g., precipitation, soil moisture, wind speed, and temperature fluctuation) and corresponding technological sensing mechanisms for the classification of risk levels and the improvement of early warning reliability. By focusing on a user-centered design and data-driven workflow, the system advances situational awareness, hastens decision-making, and closes the gap between disaster prediction and effective response. This framework enhances national and local-level disaster resilience by supporting viable risk reduction and crisis management strategies.
EL-Alfy A.E, Esmat Mona, Sakr Hagar· International Journal of Sci...· 0 citations
Against the background of global climate change and the increasing frequency of extreme weather events, the Guangdong–Hong Kong–Macao Greater Bay Area—as a typical coastal highdensity urban agglomeration—has long been exposed to compound natural disaster risks (typhoons, heavy rainfall, floods, storm surges, and geological disasters) under the combined influence of land–sea interactions, rapid urbanization, and multiple overlapping hazards. Regional disaster governance has therefore become a critical interdisciplinary issue bridging geoscience and public governance. To systematically evaluate the structural characteristics and design quality of disaster governance policies in this region, this study analyses 115 policy texts and constructs an integrated analytical framework.
We combine text mining, semantic network analysis, and the Policy Modeling Consistency (PMC) index. First, word segmentation statistics and keyword cooccurrence analysis are employed to identify policy themes and core semantic structures. Second, a PMC evaluation index system is built to quantitatively assess the policy texts. Finally, grade classification and PMC surface plots are used to compare structural differences among various policies.
The results show that disaster governance policies in the Guangdong–Hong Kong–Macao Greater Bay Area have generally formed a relatively systematic institutional framework, with most sample policies rated as “excellent” or “good.” Excellent policies perform more evenly in terms of policy instruments, policy content, supporting measures, and target coverage, whereas average policies show deficiencies in the configuration of issuing bodies, temporal arrangements, crosslevel coordination, and evaluation and feedback mechanisms.
Accordingly, this study suggests strengthening multiactor collaboration, improving closedloop policy management, and enhancing the balance and synergy of policy structures, so as to improve the governance capacity and regional resilience of the Greater Bay Area in response to compound natural disaster risks. These findings also provide a reference for disaster risk governance and regional disaster prevention and reduction policy optimization in the field of geoscience.
Shu-Min Zhao, Xi Wang, Tao Zhang et al.· Frontiers in Earth Science· 0 citations
To investigate the disaster evolution characteristics and associated risks of heavy rainfall and flooding on urban transportation infrastructure, this study takes the extreme rainstorm event in Zhengzhou as a typical case. A multidimensional dynamic risk assessment model is employed to analyze the disaster evolution process and conduct risk evaluation. First, the three-stage evolution process and its characteristics are systematically examined. Then, based on the theory of natural disaster risk elements, a dynamic risk assessment model is constructed. The improved Order of Priority Approach (OPA) is used to determine the weights of multidimensional risk factors, and interval type-1 fuzzy logic is introduced to address the uncertainty of fuzzy indicators. Finally, the overall risk level of the heavy rainfall–flooding disaster chain is calculated and evaluated. The results indicate a high-risk level, which is consistent with the findings of the field investigation report, thereby validating the feasibility of the proposed disaster chain evaluation method combining multiple models. This analysis provides a theoretical basis for future studies on similar urban storm flood risk scenarios.
Yongcheng Zhang, Jianwei Wang, Ziyi Wu et al.· SAE technical paper series· 0 citations
Flooding causes substantial loss of life, infrastructure damage, livelihood disruption, and socio-economic impacts, highlighting the need for timely and localized early-warning systems. This study proposes the Integrated Flood Alert System (IFAS), a conceptual framework integrating environmental sensing, historical flood information, location-specific thresholds, risk assessment, and coordinated warning communication. IFAS addresses three flood pathways: direct rainfall, floodwater propagation from adjacent or upstream areas, and drainage overflow. The framework was examined using the 2018 Kerala floods and 2024 Dubai flood event as contextual case studies, alongside a community survey of 160 respondents assessing flood-risk perceptions and acceptance of integrated warning technologies. 87.5% of respondents expressed concern about flood risk, and 98.1% considered an integrated flood-alert system beneficial for preparedness and loss reduction. IFAS provides a scalable framework for integrating multi-pathway monitoring with localized risk assessment and coordinated warning dissemination. However, its predictive accuracy and operational effectiveness remain unvalidated. Field-based evaluation, sensor and threshold calibration, warning-lead-time assessment, and comparative performance testing are required before operational deployment.
Ananya K. Jinesh, Arathi Subhash· IJRDO - journal of applied s...· 0 citations