Comprehensive Disaster Research Articles

Maslow’s hierarchy of needs: a critical examination in disaster situations

ABSTRACT Climate-related disasters are critical global challenges, and social work is vital in supporting individuals and communities to promote their well-being in response. This empirical research examines how Maslow’s hierarchy applies to social service provision in Oman following climate events, focusing on the framework’s potential and limitations within an Islamic cultural setting. This qualitative study employs 43 semi-structured interviews with a diverse group, including senior managers, social workers, non-governmental organization workers, religious leaders, and service users. Through thematic analysis, the data shows significant similarities between the needs of people affected by climate events and Maslow’s theory, which has led the state to provide services based on this hierarchy. However, the study emphasizes that in an Islamic society like Oman, spiritual needs are not confined to the higher levels of the hierarchy but must be addressed at all levels. This means religious practitioners should be involved in relief efforts from the beginning of such events. The study also illustrates the importance of maintaining robust communication networks, which should be considered a fundamental necessity. Policy, practice, and educational implications are discussed, proposing adaptations to Maslow’s hierarchy of needs within social work curricula and advocating for integrating spiritual needs into comprehensive disaster management frameworks.

Hydrometeorological Disasters in the Context of the Climate Crisis: A Regional Disaster Inventory Study in Türkiye

The rapid growth of the global population, irregular urbanization and industrialization, present challenges to the sustainability of life and the world is currently facing an increasing prevalence of both natural and anthropogenic disasters. Climate change further exacerbates this situation by contributing to the rising frequency and intensity of hydrometeorological events. To effectively minimize the negative impacts of disasters, knowing assessing and analyzingtheir frequency, intensity, and consequences is essential. This necessitates the development of comprehensive disaster inventories. Such studies involve collecting and analyzing critical data to identify and manage potential disaster risks within specific regions. In this present study evaluates the long-term diversity of hydrometeorological disasters in the Sakarya, Kocaeli, and Istanbul provinces, situated in the Çatalca-Kocaeli section in the Marmara Region of Turkey. The study utilizes data from the General Directorate of Meteorology to analyze hydrometeorological disasters occurring from 1975 to 2020 across these provinces, with a focus on diversity, frequency, damages, and fatalities, evaluated on both an annual and seasonal basis. Damages are classified as light, moderate, or severe. This inventory is designed to offer valuable insights for researchers and policymakers aimed at mitigating loss of life and property damage. Additionally, it encompasses recommendations for disaster risk reduction strategies.

When disaster strikes a smart tourism city: tourists narratives of Dubai’s recent flooding

ABSTRACT This study addresses a significant gap in tourism disaster management by analyzing the firsthand experience of international tourists during the unprecedented recent flooding in Dubai, a smart tourism city. The study uses narrative inquiry to capture tourists’ personal accounts, perceptions of the disaster's impact, and observations of local response efforts. The goal is to assess the resilience of smart tourism cities and the effectiveness of their disaster management strategies. Employing semi-structured interviews, the research collected detailed narratives from 26 tourists impacted by the flooding, offering an in-depth understanding of their experiences and the operational responses from disaster management teams. The findings reveal substantial disruptions in travel and communication, highlighting deficiencies in the current infrastructure and disaster management systems for handling large-scale emergencies. Tourists’ narratives indicate a blend of preparedness and oversight, with some infrastructure elements functioning well while others, especially in safety and timely communication, fell short. This study emphasizes the necessity for comprehensive disaster management resilience strategies that enhance infrastructure robustness, improve communication systems, and actively involve all stakeholders to boost the resilience and safety of smart tourism cities.

A Review of the Application of Artificial Intelligence in Climate Change-Induced Flooding—Susceptibility and Management Techniques

A fresh paradigm for classifying current studies on flood management systems is proposed in this review. The literature has examined methods for managing different flood management activities from a variety of fields, such as machine learning, image processing, data analysis, and remote sensing. Prediction, detection, mapping, evacuation, and relief efforts are all part of flood management. This can be improved by adopting state-of-the-art tools and technology. Preventing floods and ensuring a prompt response after floods is crucial to ensuring the lowest number of fatalities as well as minimizing environmental and financial damages. The following noteworthy research questions are addressed by the framework: (1) What are the main methods used in flood control? (2) Which stages of flood management are the majority of research currently in existence focused on? (3) Which systems are being suggested to address issues with flood control? (4) In the literature, what are the research gaps regarding the use of technology for flood management? To classify the many technologies that have been studied, a framework for classification has been provided for flood management. It was found that there were few hybrid models for flood control that combined machine learning and image processing. Furthermore, it was discovered that there was little use of machine learning-based techniques in the aftermath of a disaster. To provide efficient and comprehensive disaster management, future efforts must concentrate on integrating image processing methods, machine learning technologies, and the understanding of disaster management across all phases. The study has proposed the use of Generative Artificial Intelligence.

Building Resilience: The Role of Community-Based Disaster Preparedness Training in Empowering Lubuk Village, Indonesia

Lubuk Village in Bungo Regency, Jambi, Indonesia, faces a range of potential natural hazards, including floods, landslides, and forest fires. Building community resilience is crucial to mitigate the impact of these disasters. This community service initiative focused on empowering residents of Lubuk Village through a comprehensive disaster preparedness training program. A participatory approach was adopted, involving collaboration with village leaders, community members, and local government agencies. The training program consisted of workshops, simulations, and hands-on activities covering hazard identification, risk assessment, early warning systems, evacuation procedures, first aid, and basic search and rescue techniques. The training program enhanced community awareness of disaster risks and strengthened their capacity to respond effectively to emergencies. Participants demonstrated increased knowledge, improved skills, and greater confidence in their ability to protect themselves and their families during disasters. The initiative also fostered community cohesion and strengthened social networks. In conclusion, community-based disaster preparedness training is an effective public administration intervention for empowering communities to face natural hazards. By equipping residents with knowledge and skills, this initiative contributed to building a more resilient Lubuk Village.

Unveiling Resilience: Navigating the Nexus of Climate Change, Disaster Mitigation, and Social Work Education in Flood-Prone Sri Lanka

This scholarly inquiry explores the intricate dynamics of climate change, disaster mitigation frameworks, and the vital role of social work education in building resilience amidst flood disasters in Sri Lanka. The study meticulously dissects inundation events, revealing dimensions that underscore community vulnerability. Through a discerning evaluation of disaster mitigation paradigms, the study identifies gaps and strengths shaping the current resilience landscape. A focal point emerges in a comprehensive investigation into the transformative potential of social work education, acting as a catalyst to amplify community resilience. This involves exploring educational frameworks, assessing practices' effectiveness, and delineating the role of social work professionals in instigating transformative change within vulnerable communities. The research design, characterized by methodological rigor, unfolds through granular analysis concentrated on flood-prone areas in Sri Lanka. This nuanced examination distills findings beyond statistics, providing a deeper understanding of socio-cultural and environmental factors influencing community resilience. Anticipated scholarly contributions aim to transcend the academic realm, informing contextually nuanced strategies. Envisioned strategies, designed to seamlessly integrate climate change awareness and social work methodologies, aspire to become integral components of overarching disaster response frameworks. The academic impact sought extends beyond theory, aiming to propel practical advancements by accentuating the imperative of interdisciplinary collaboration. The discourse on comprehensive disaster management is advanced, emphasizing symbiotic relationship between research, education, and practical interventions. Ultimately, findings from this pursuit are poised to provide actionable insights, significantly contributing to the cultivation of adaptive and resilient communities grappling with the challenges posed by the dynamic forces of climate change.

Network of the public administration of zoos in the Sustainable Development Goals

Within the framework of the effects of climate change, the public administration of zoos is a scenario to observe the relationship between Sustainable and Human Development. Based on the analysis of risks and vulnerabilities, the management of recreational centers makes its visitors aware of the importance of the Sustainable Development Goals (SDG). The objective of this work was to establish the network of perceptions around the public administration of species in captivity and for investigative or cultural purposes. An exploratory, correlational and cross-sectional work was carried out with a sample of 100 professional practitioners and social servants from centers of captivity of endangered species. The results show that the dimensions of public administration were associated with SDGs 1, 5, 7, 12 and 15 related to the end of poverty, gender equality, affordable and clean energy, responsible production and consumption, life in terrestrial ecosystems. In relation to the studies of Comprehensive Disaster Risk Management (GIRD), lines of study are recommended for the analysis of risks and vulnerabilities in scenarios of captivity of species.

Evaluating the Dynamic Comprehensive Resilience of Urban Road Network: A Case Study of Rainstorm in Xi’an, China

Rainstorms and flooding are among the most common natural disasters, which have a number of impacts on the transport system. This reality highlights the importance of understanding resilience—the ability of a system to resist disruptions and quickly recover to operational status after damage. However, current resilience assessments often overlook transport network functions and lack dynamic spatiotemporal analysis, posing challenges for comprehensive disaster impact evaluations. This study proposes an SR-PR-FR comprehensive resilience evaluation model from three dimensions: structure resilience (SR), performance resilience (PR), and function resilience (FR). Moreover, a simulation model based on Geographic Information System (GIS) and Simulation of Urban MObility (SUMO) is developed to analyze the dynamic spatial–temporal effects of a rainstorm on traffic during Xi’an’s evening rush hour. The results reveal that the southwest part of Xi’an is most prone to being congested and slower to recover, while downtown flooding is the deepest, severely affecting emergency services’ efficiency. In addition, the road network resilience returns to 70% of the normal values only before the morning rush the next day. These research results are presented across both temporal and spatial dimensions, which can help managers propose more targeted recommendations for strengthening urban risk management.

Disaster Management Strategy Through Social Capital Development

Indonesia is one of the countries with the highest level of natural disaster vulnerability in the world due to extreme geographical and climate conditions. In facing this risk, the government has implemented a disaster management policy based on community involvement. One important aspect in this approach is social capital, which includes social participation, interpersonal trust, and community networks, as a crucial component to strengthen community resilience. This study explores the role of the Meteorology, Climatology, and Geophysics Agency (BMKG) in Lampung Province in developing weather literacy and disaster mitigation programs through digital media to build social capital in the community. The results of the study show that although BMKG has succeeded in increasing community participation through digital channels, community involvement is currently still passive and limited. To achieve comprehensive disaster preparedness, it is necessary to increase active community participation, strengthen trust through two-way communication, and expand community networks. With a social capital-based approach, BMKG is expected to be able to create a more responsive and adaptive mitigation program, making the community an active partner in facing the threat of disasters in the future.

Strengthening Resilience and Sustainability for Post-Disaster Recovery: A Comparative Law and Economics Analysis on Smart Mixes Between Mechanisms

This article advocates smart mixes of mechanisms of post-disaster recovery in terms of boosting resilience and sustainability through liability rules, government intervention, insurance, and their combinations. Liability rules can provide compensation to victims and, in theory, also reduce disaster risks, while they have their limits in practice. The government widely intervenes in disaster compensation in many countries, but it faces challenges in how it can intervene fairly and efficiently, whether through informal channels, such as ad hoc charity, or structured approaches, such as compensation funds. As governments may struggle to provide efficient and effective compensation ex post, insurance may offer proactive solutions with models of first-party catastrophe insurance and third-party liability insurance ex ante. Where market failure, liability failure, and government failure may all arise, a smart mix of mechanisms is often preferable from a law and economic perspective, rather than relying solely on one framework. However, upon examining empirical evidence regarding the effectiveness of these instruments in China, the UK, France, Germany, and Turkey, it becomes apparent that a mix of mechanisms is not always applied in an effective manner. Models of mandatory comprehensive disaster insurance, and Public–Private Partnership (PPP), which both receive government intervention as a last resort to the market and liability failures, can reach the goal of more effective compensation for disaster victims, risk prevention and resilience when faced with disaster recovery, and should therefore be substantially implemented beyond the current levels. To be clear, the proposed mix of solutions mainly focuses on legal and economic dimensions which are rather limited, ignoring, for example, building codes for structures and physical interventions.

Feasibility of Emergency Flood Traffic Road Damage Assessment by Integrating Remote Sensing Images and Social Media Information

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, the surface area affected by precipitation is extracted using a threshold method constrained by confidence intervals derived from microwave remote sensing images. Second, disaster information is collected from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms (TextCNN-Attention disaster information classification) was proposed. Finally, traffic road information on social media is matched with basic geographic data, the classification of traffic road disaster risk levels is visualized, and the assessment of traffic road disaster levels is completed based on multi-source heterogeneous data. Using the “7.20” rainstorm event in Henan Province as an example, this research categorizes the disaster’s impact on traffic roads into five levels—particularly severe, severe, moderate, mild, and minimal—as derived from remote sensing image monitoring and social media information analysis. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

Structural resilience disparities in regional city networks under the influence of Typhoon Fitow and COVID-19

The integration of regional city networks plays a pivotal role in the regional development. Assessing and analyzing the structural resilience of these networks holds significance in enhancing regional comprehensive disaster prevention. This study focuses on the Yangtze River Delta, examining its regional city networks encompassing information, economic, transportation, and innovation domains. Utilizing the social network analysis, projection pursuit model based on the genetic algorithm, coupling coordination degree model, coefficient of variation and spatial Gini coefficient, we scrutinized the structural resilience and evolution difference of these networks under different disasters. We found that the structural resilience of economic and transportation networks that depend on infrastructure construction are more limited by geographical distance, and information and innovation networks with weaker physical dependence have stronger structural resilience. The impact of Typhoon Fitow on the structural resilience of individual networks was mainly concentrated in coastal affected cities, while the impact after COVID-19 was mainly in marginal cities. The spatial pattern of the structural resilience of multiple networks shifted from low level equilibrium to agglomeration development. Finally, nodes were identified into three types according to the resilience performance. This research offers a scientific foundation for quantifying regional resilience and formulating network optimization strategies.

Kilometer-Scale Precipitation Forecasting Utilizing Convolutional Neural Networks: A Case Study of Jiangsu’s Coastal Regions

High-resolution precipitation forecasts play a pivotal role in formulating comprehensive disaster prevention and mitigation plans. As spatial resolution enhances, striking a balance between computation, storage, and simulation accuracy becomes imperative to ensure optimal cost-effectiveness. Convolutional neural networks (CNNs), a cornerstone of deep learning, are examined in this study for their downscaling capabilities in precipitation simulation. During a precipitation event on 23 June 2022, in Jiangsu Province, China, distinct rain belts emerged in both southern and northern Jiangsu, precisely captured by a numerical model (the Weather Research and Forecasting, WRF) with a 3 km spatial resolution. Specifically, precipitation was prevalent in northern Jiangsu from 00:00 to 11:00 Beijing Time (BJT), transitioning to southern Jiangsu from 12:00 to 23:00 BJT on the same day. Upon dynamic downscaling, the model reproduced precipitation in these periods with an average error of 12.35 mm at 3 km and 12.48 mm at 1 km spatial resolutions. Employing CNN technology for statistical downscaling to a 1 km spatial resolution, samples from the initial period were utilized for training, while those from the subsequent period served for validation. Following dynamic downscaling, CNNs with four, five, six, and seven layers exhibited average errors of 8.86 mm, 8.93 mm, 9.71 mm, and 9.70 mm, respectively, accompanied by correlation coefficients of 0.550, 0.570, 0.574, and 0.578, respectively. This analysis indicates that for this precipitation event, a shallower CNN depth yields a lower average error and correlation coefficient, whereas a deeper architecture enhances the correlation coefficient. By employing deep network architectures, CNNs are capable of capturing nonlinear patterns and subtle local features from complex meteorological data, thereby providing more accurate predictions during the downscaling process. Leveraging faster computation and reduced storage requirements, machine learning has demonstrated immense potential in high-resolution forecasting research. There is significant scope for advancing technologies that integrate numerical models with machine learning to achieve higher-resolution numerical forecasts.

Smart Green Concrete: Innovation of Concrete Materials from Fly ash Waste and Lapindo Mud Integrated Smart E. crassipes Coir Geotextile to Realize Indonesia's Sustainable Infrastructure

Pariaman City, situated near the Semangko fault and the Indo-Australian plate subduction zone, is highly prone to earthquakes and tsunamis. To address this, the Earthquake Buddy Application (EDY App) was developed as an innovative solution for disaster mitigation. EDY App utilizes Geographic Information Systems (GIS) to provide real-time earthquake vulnerability maps, early warnings, and evacuation route suggestions to Temporary Evacuation Sites (TES). It also offers education on earthquake preparedness and facilitates donations for relief efforts. The application’s main contribution lies in its integration of comprehensive disaster management tools into one platform. By providing critical information during emergencies, the app empowers users to make informed decisions, which can reduce casualties in high-risk areas. The app also encourages community preparedness by offering accessible education on earthquake mitigation. EDY App is designed to create a disaster-resilient society in Pariaman, ensuring that residents are better equipped to respond to natural disasters through real-time alerts and evacuation support, ultimately enhancing local preparedness and reducing the potential impact of future earthquakes.

Disaster Preparedness among Households in the Municipality of Pura, Tarlac: An Analysis

This research endeavors to evaluate the level of disaster preparedness among households in the Municipality of Pura, Tarlac, employing a quantitative descriptive research approach. A survey questionnaire was utilized to gather data from 112 household respondents, selected through random sampling across barangays most vulnerable to disasters. Data analysis, facilitated by SPSS analytical statistics software, focused on identifying trends and correlations between disaster preparedness levels and encountered challenges. The findings reveal both strengths and challenges in disaster preparedness. While emergency plans, risk awareness, and response capacity demonstrate commendable efficacy, deficiencies in emergency supplies, community engagement, and infrastructure persist. Correlation analysis underscores the positive relationship between proactive measures and mitigation of challenges, emphasizing the importance of investing in comprehensive disaster management strategies. Recommendations are proposed based on the research findings. Strengthening emergency supplies and resources through equitable distribution mechanisms and community collaboration is advised. Fostering greater community engagement and collaboration, enhancing emergency plans, and improving response and recovery capacity are also recommended. These measures, encompassing workshops, collaboration initiatives, and resource provisions, aim to empower households with the necessary knowledge and resources for effective disaster preparedness, response, and recovery. Implementation of these recommendations is crucial for enhancing disaster resilience and readiness in the Municipality of Pura, Tarlac. By addressing identified challenges and bolstering proactive measures, the community can better safeguard its residents and infrastructure against future disasters, ensuring their safety and well-being.

Assessing the Effects of Citizen Climate Literacy and Attitudes on Their ‘Greening’ Behaviour in a Climate Change Hotspot Region of the Eastern Mediterranean

Climate change presents an urgent global challenge, manifesting in rising temperatures and extreme weather events with severe societal impacts. The Eastern Mediterranean, warming faster than the global average, faces immediate repercussions. Climate literacy emerges as pivotal, empowering individuals to comprehend climate science and act accordingly. This study delves into climate literacy, attitudes, and ‘greening’ behaviours in the Eastern Mediterranean hotspot of Greece, based on a survey of 1962 citizens. Findings indicate high climate literacy but lower adoption of ‘greening’ behaviours, especially those involving financial costs. Regression analyses highlight the significant role of climate literacy, concerns about personal impacts, coping appraisal, and trust in institutions in promoting ‘greening’ behaviours. This study underscores the need for multifaceted strategies emphasising financial motivation, trust-building, and societal norm shifts. Socio-demographic disparities, including gender and occupation, highlight areas for targeted interventions. The emphasis on the mental health impacts of climate-related events underscores the need for comprehensive disaster management that addresses not only physical damage but also psychological and social dimensions. Policy implications are discussed, highlighting the potential of expanded climate literacy to catalyse collective action toward sustainability.

Assessment of disaster mitigation capability oriented to typhoon disaster chains: A case study of Fujian Province, China

Typhoon disasters are the most frequent and severe natural disasters in China’s southeastern coastal region. The strong wind and rainstorms during typhoons result in secondary disasters, such as storm surges, floods, and landslides. This phenomenon is referred to as a typhoon disaster chain that causes significant loss of life and property every year. Accurately assessing the disaster mitigation capacity and reducing limitations are crucial for improving typhoon disaster risk prevention. However, assessing the capacity of mitigating typhoon disaster chains requires further study. In this study, we select Fujian Province, China, as a case study to identify typical typhoon disaster chains, drawing on historical disasters and the sensitivity of the disaster-forming environment. We establish a county-based framework to assess the disaster mitigation capabilities for typhoon disaster chains. The mitigation capacities of 84 counties within Fujian Province are evaluated using the Random Forest (RF) algorithm, which determines the weights of various indicators. The results show the following. 1) The dominant types of typhoon disaster chains in Fujian Province are typhoon-rainstorm-urban waterlogging (TRU), typhoon-rainstorm-flooding (TRF), typhoon-rainstorm-landslide (TRL), and typhoon-strong wind-storm surge (TWS). Significant spatial differences are observed. 2) The assessment framework, which includes disaster prevention, disaster relief, and government management capabilities, accurately reflects the spatial differences in the disaster chain. The results can be extended to other regions or other disaster chains. 3) Significant spatial heterogeneity is observed in the disaster prevention, relief, and disaster mitigation capacity for typhoon disaster chains in the counties in Fujian Province. The eastern coastal areas have high mitigation capacity, whereas the northwest has low capacity. The disaster prevention capacity is very high in Changle City and Xiuyu District (2.4 %), and the disaster relief capacity is very high in Gulou District, Taijiang District, Changle City, and Huli District (4.8 %). The government disaster management capacity is very high in 7 counties (e.g., Longhai City, Minhou County, and Hui’an City (8.3 %)). The comprehensive disaster mitigation capabilities are very high in Changle City, Longhai City, and Xiuyu County (3.6 %). This study provides a scientific reference for assessing disaster chain mitigation capabilities and enhancing grassroots disaster mitigation efforts.

Disaster Recovery of Near Field Communication (NFC) Systems

This paper explores the vulnerabilities inherent in NFC systems and presents a comprehensive disaster recovery framework designed to enhance resilience. By reviewing existing literature on disaster recovery, analyzing past failures of NFC systems, and proposing strategies such as data backups, real-time monitoring, and blockchain integration, this study aims to bolster the reliability of NFC systems and ensure their continued operation during crises.

Disaster loss index development and comprehensive assessment: A case study of Shanghai

Accurate measurement of socio-economic loss and the impact of disasters is crucial for effective risk assessment and management. This study introduces a Disaster Severity Index (DSI) and a Disaster Impact Index (DII), along with a composite severity matrix, to evaluate comprehensive disaster losses. By utilizing seven loss indicators across six types of natural disasters and socioeconomic indicators, we thoroughly assessed Shanghai’s historical disaster losses from 1978 to 2020. Our analysis reveals that Shanghai’s most severe annual disaster losses occurred in 1991 and 2005; however, the overall impact on the local socio-economy has been declining. According to the DSI, Chongming (0.88), Songjiang (0.64), and Qingpu (0.51) Districts exhibit the highest severity. In terms of the DII, Songjiang (0.87), Chongming (0.71), and Qingpu (0.64) Districts are most affected. The comprehensive disaster severity grading indicates that Chongming and Songjiang Districts experience the most severe impacts, while Huangpu and Xuhui Districts are less affected. The spatial distribution of disaster losses is concentrated in suburban areas, with less impact in the central city. Our methodology provides a framework for analyzing and comparing historical disaster losses in terms of time (inter-annual), space (administrative divisions), and types of disasters. It can also be used for statistical analysis and regular monitoring of disaster losses. This study not only elucidates the spatiotemporal distribution of historical disaster losses in Shanghai but also validates the efficacy of our comprehensive index models.

Improving Disaster Response in Indonesia Through Culturally Integrated Communication Strategy

This study aims to analyze the communication strategies employed by the Regional Disaster Management Agency (BPBD) in disaster risk reduction efforts in Sidoarjo. Effective communication is crucial in ensuring community preparedness and resilience against disasters. This research adopts a qualitative method with a case study approach, using in-depth interviews, documentation, and literature review as data collection techniques. The primary data were analyzed using thematic analysis to identify critical patterns and insights. The results reveal that BPBD employs various communication strategies, including social media, public campaigns, and collaboration with local leaders. These efforts are designed to increase public awareness, disseminate early warning information, and foster community participation in disaster preparedness programs. Challenges identified include limited access to remote areas, inadequate resources, and low public engagement due to cultural and socio-economic factors. The discussion emphasizes the importance of integrating local wisdom and community-based approaches to enhance the effectiveness of disaster communication. The study concludes that combining modern technology and traditional communication methods is essential for comprehensive disaster risk reduction. Strengthening stakeholder collaboration and investing in capacity-building initiatives are also recommended for future improvements.