Disaster Management Research Articles

Enhancing gender-responsive resilience: The critical role of women in disaster risk reduction in Oman

Successful disaster risk reduction (DRR) and preparedness require the active participation of all genders and age groups. However, gender roles and relations are often overlooked in disaster management. This study explores the critical role of women in enhancing gender-responsive resilience within DRR in Oman. It highlights existing gender disparities in DRR strategies and emphasizes the need for inclusive policies that leverage women's knowledge, leadership, and adaptive capacities. By examining Oman's experience with natural disasters (cyclones) and COVID-19, the research underscores the importance of integrating gender perspectives in DRR for more effective outcomes. Utilizing a quantitative survey and statistical analysis, the study investigates variables shaping women's participation in DRR, including knowledge, communication, experience, social protection, and voluntarism. Findings demonstrate the crucial role of women in enhancing disaster resilience and their potential in mitigating disaster risks effectively. Policy recommendations advocate for empowering women and promoting their active participation in DRR to strengthen community resilience and contribute to broader socio-economic development goals in Oman. This study calls on policymakers, practitioners, and stakeholders to support the pivotal role of women in building resilient societies and to adopt a more equitable approach to disaster management.

A Scoping Review of Nurses' Knowledge and Preparedness in Disaster Management in Saudi Arabia

Nurses are the front-line responders to disasters. Providing them with sufficient knowledge, training, and preparedness is essential. Saudi Arabia, a country prone to numerous disasters, needs to enhance the training and preparedness of nurses and first responders to effectively respond to emergencies and minimize the impact on lives and the economy. The aim of this scoping review is to evaluate the preparedness of nurses and identify emerging trends in disaster nursing in Saudi Arabia. This review will serve as a foundation for future rigorous studies. A comprehensive search strategy was developed and implemented, sourcing studies from various online databases. Each study was evaluated for inclusion, followed by a critical appraisal. The key characteristics of the 13 included studies were charted, and the studies were then mapped onto the ICN framework. The key themes and patterns emerging from the appraisal were analyzed using the PAGER framework. In the discussion, the PAGER framework was expanded, incorporating insights from existing literature to identify gaps and develop avenues for future research. The implications of this research include recommendations to focus on experimental studies that investigate the most effective strategies for improving disaster preparedness among nurses.

Fuzzy Logic-Based Landslide Susceptibility Mapping in Earthquake-Prone Areas: A Case Study of the Mila Basin, Algeria

Abstract —This research focuses on analyzing landslides triggered by a moderate earthquake (Mw = 4.9) in the northeastern region of the Mila province, which resulted in significant damage and economic losses in the El Kherba district and Grarem Gouga city. Through an extensive field-based investigation, a comprehensive inventory of landslides has been compiled. To assess the susceptibility to landslides triggered by seismic activity, a GIS-based fuzzy logic model was employed. The model incorporates various input factors, such as lithology, slope angle, normalized difference vegetation index (NDVI), distance from rivers and roads, precipitation, and seismic hazard, which is shown on a map. The study compares the performance of different fuzzy operators and gamma values and determines that using fuzzy gamma operators with a gamma value of 0.8 yields a satisfactory consistency with the distribution of landslides. Moreover, incorporating the map of seismic hazard as a causative factor enhances the accuracy of landslide susceptibility mapping. This study underscores the utility of the fuzzy logic model in disaster management and the planning of development activities.

AUTONOMOUS MONITORING ROBOT SYSTEM THAT MEASURES AIR POLLUTION

Air pollution poses a significant threat to human health, ecosystems, and climate stability, necessitating effective monitoring and control measures. Traditional air quality monitoring stations, while accurate, are static, expensive, and limited in coverage. The Autonomous Monitoring Robot System (AMRS) provides a dynamic solution, offering real-time air quality monitoring through mobile robotic platforms. Equipped with advanced sensors, these robots can measure various pollutants, such as particulate matter (PM), nitrogen dioxide (NO2), carbon monoxide (CO), and volatile organic compounds (VOCs), across large and complex areas. By employing AI-based navigation and mapping technologies, AMRS can autonomously traverse urban and industrial environments, collecting high-resolution pollution data and creating real-time air quality maps. This approach allows for better spatial coverage, cost-efficiency, and access to hard-to-reach locations compared to traditional methods. The system can be deployed in diverse use cases, including urban air quality monitoring, industrial pollution control, and disaster management. While challenges such as battery life, sensor calibration, and data processing remain, AMRS represents a promising technological advancement in environmental monitoring and pollution control.

Enhancing land cover classification via deep ensemble network

The rapid adoption of drones has transformed industries such as agriculture, environmental monitoring, surveillance, and disaster management by enabling more efficient data collection and analysis. However, existing UAV-based image scene classification techniques face limitations, particularly in handling dynamic scenes, varying environmental conditions, and accurately identifying small or partially obscured objects. These challenges necessitate more advanced and robust methods for land cover classification. In response, this study explores ensemble learning (EL) as a powerful alternative to traditional machine learning approaches. By integrating predictions from multiple models, EL enhances accuracy, precision, and robustness in UAV-based land use and land cover classification. This research introduces a two-phase approach combining data preprocessing with feature extraction using three advanced ensemble models DenseNet201, EfficientNetV2S, and Xception employing transfer learning. These models were selected based on their higher performance during preliminary evaluations. Furthermore, a soft attention mechanism is incorporated into the ensembled network to optimize feature selection, resulting in improved classification outcomes. The proposed model achieved an accuracy of 97 %, precision of 96 %, recall of 96 %, and an F1-score of 97 % on UAV image datasets. Comparative analysis reveals a 4.2 % accuracy improvement with the ensembled models and a 1 % boost with the advanced hybrid models. This work significantly advances UAV image scene classification, offering a practical solution to enhance decision-making precision in various applications. The ensemble system demonstrates its effectiveness in remote sensing applications, especially in land cover analysis across diverse geographical and environmental settings.

Natural Hazard Induced Coastal Vulnerability in Indian Sundarbans: A Village-level study by using Geospatial and Statistical Techniques

The Sundarbans, an area with a history dating back to the dawn of civilization, has faced numerous environmental hazards that have remarkably affected the lives and livelihoods of its natives. Strom surge and Tropical cyclones are the most substantial natural hazards, causing severe damage to local communities by affecting food security, the economy, shelter and health. By defining vulnerability as a function of exposure, sensitivity, and resilience capacity, we calculated a composite vulnerability index (CVI) using equal weight method (EWM) to assess the vulnerability of mouzas (small administrative units) to natural hazards in the Sundarbans, India. The vulnerability map has been drawn based on composite value of CVI which shows 30.50% of villages falling into the high vulnerability category and 12.06% in the very high vulnerability category. The mouza-level analysis also indicates that 22.62% of Sundarbans’s villages are highly exposed to natural hazards and 19.70 % of villages are classified as being at Very high sensitive. Only 7.07% villages have very high adopted capacity against these natural hazards. Villages in the southern parts and along the coast were found to be more vulnerable to storm surges. Conversely, those situated at higher elevations in the central area exhibited lower vulnerability. In the northern part of the region, several villages faced high to very high vulnerability due to low-lying, waterlogged wetlands. This study offers vital insights for decision-makers, government planners, and disaster management professionals, assisting in the identification of high-risk populations and areas that require immediate preservation efforts.

Geospatial analysis of alarmingly increasing flood vulnerability and disaster risk within the northeast Himalaya Region of India

Geoenvironmetally the eastern part of the Himalaya region is highly vulnerable to flood and other natural disasters as it consists of fragmented and tectonically active geology and geomorphology, very high monsoon rainfall (>360cm) and subsequent runoff, rugged hilly terrain with high ranges of elevation and slope, dense drainage density etc. Other hand, the unplanned developmental activities keep going and scaling up this vulnerability and risk to floods and other disasters. Addressing this burning issue, a geospatial technology-based case study of the Kohima district, Nagaland state (India), is presented here. The geospatial "technology-based" analyses employed in the study are thematic GIS mapping of flood hazard, vulnerability and risk controlling factors; and performing overlay operation using the AHP model in GIS software to generate spatio-temporal map layers of flood vulnerability and disaster risk. Results reveal that the region is under a high rate of monsoon climate change (increasing temperature, rainfall, rainy days, rainfall events and flood events with an annual rate of 0.35%, 1.12%, 0.36%, 2.67% and 4% respectively), land use degradation (increasing built-up area with 0.60%, annual rate decreasing forest, shrubs and water bodies with 0.80% accumulated annual rates respectively) and demographic changes (increasing urban as well and rural population density with 0.53-2.10% and 0.55-2.14% respectively). Accumulated impacts of climate change, land use degradation and demographic changes causing an increase in flood hazard, vulnerability and disaster risk. Flood hazard zones and vulnerability zones extending with 0.50% (4.89km2) and 0.84% (8.16km2) annual rates respectively, subsequently the food risk zones categorized as a moderate, high and very high potential risk, have been spreading out with a yearly rate of 0.07% (0.65km2), 0.13% (1.31km2) and 0.03% (0.33km2) respectively. It decreases the area under low and very low-risk zones by 0.17% (1.63km2) and 0.07% (0.65km2) annual rates respectively. Following up on these annual rates, the spatial distribution of flood hazard, vulnerability and risk zones for the next decade (2031-2040) have also been projected, revealing alarming situations, if flood disaster risk reduction (F-DRR) measures were not implemented in timely. It is strongly believed that the proposed study will be very useful for district-level planners and administrators to implement sustainable development planning, for the scientific fraternity to enhance their research work in the field of flood disaster management and for individuals for their safety in terms of life and property.

Improving the UNet with channel attention mechanism for Taihu Lake water body recognition

Abstract Accurate recognition of surface water bodies is important for disaster management and resource planning. To explore a higher-precision water body recognition method, propose an improved channel attention mechanism UNet (ECA-UNet) water body recognition model based on Sentinel-2A remote sensing images. Utilizing ECA-UNet, UNet, PSPnet, and DeeplabV3, as well as the traditional normalized water body index (NDWI), recognize the water body in the Taihu Lake area. Through comparative analysis, the following main conclusions are drawn: (1) The recognition accuracy of the deep learning network model is better than the traditional NDWI method, among which the ECA-UNet network model has the highest accuracy of 98.67%, and the lowest recognition accuracy of NDWI is 89.06%. (2) The ECA-UNet network model can effectively extract water body recognition targets, has a better recognition effect on local features of water bodies, and can improve the water body extraction effect. The above results show that the improved channel attention mechanism ECA-UNet has the highest accuracy in water body recognition in Taihu Lake, which provides a reference for subsequent research and applications in this field.

Remote sensing assessment of wildfire using high-resolution PlanetScope satellite observations: A case study on Co Tien Mountain, Nha Trang City, Vietnam

In this study, high spatial resolution (3 m) PlanetScope (PS) imagery was utilized to map burned areas caused by a wildfire occurring on January 10, 2024, on Co Tien Mountain in Nha Trang city, Khanh Hoa province, South Central Coast of Vietnam. A pre-fire image, acquired ten days earlier, on December 31, 2023, and a post-fire one, acquired nearly one month after, on February 04, 2024, were used to create pre- and post-fire Normalized Difference Vegetation Index (NDVI) maps of the study area, then the difference of NDVI (dNDVI). A threshold (T = 0.20), proposed by the author, was applied to the histogram of the dNDVI product to classify the study area into two clusters: burned pixels (dNDVI > T) and unburned pixels (dNDVI <= T). Classification results estimate that a total of 16.11 ha of grass, reeds, small shrubs and vegetation have been burned out during the wildfire. A field trip is required to map the burned areas using unmanned aerial vehicles (UAVs) for an accurate validation of results derived purely from PS satellite observations. Although lacking a ground truth dataset for validation is a significant limitation, the proposed approach remains beneficial for local managers and decision-makers. It enables the rapid assessment of damages caused by small wildfires and provides essential data for effective disaster management and recovery planning, particularly in remote areas.

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.

Erratum for “Study on the Prototype of Platform Tools and Practice Related to the ‘Sustainability Scoring Index’ for Disaster Resilience of Local Governments in Japan” (Vol.19, pp. 631-644, 2024)

Due to an authors’ error, the original version of this article contained error in the in-text citations for tables in Section 4.1. The referenced table number should be corrected as follows: Now reads The self-assessment by the officials in charge of disaster management in the local governments was based on the Sustainability Scoring Index that was formulated by the authors for this study (Table 3). Should read The self-assessment by the officials in charge of disaster management in the local governments was based on the Sustainability Scoring Index that was formulated by the authors for this study (Table 2). The authors regret this error, and this error has now been corrected in the PDF version of the article.

Impact of Summer North Atlantic Sea Surface Temperature Tripole on Precipitation over Mid–High-Latitude Eurasia

Abstract Eurasia is a sensitive and high-risk region for global climate changes, where climate anomalies significantly influence natural ecosystems, human health, and economic development. The North Atlantic tripole (NAT) sea surface temperature anomaly is crucial to interannual precipitation variations in Eurasia. Several studies have focused on the link between the NAT and climate anomalies in winter and spring. However, the mechanism by which the summer NAT impacts climate anomalies in Eurasia remains unclear. This study examines how the NAT impacts interannual variations of summer precipitation in mid–high-latitude Eurasia. Precipitation variations are associated with the atmospheric teleconnection triggered by the NAT. When the NAT is in its positive phase, the anomalous atmospheric diabatic heating over the North Atlantic excites an equivalent-barotropic Rossby wave train response that propagates eastward toward Eurasia, resulting in atmospheric circulation anomalies over the region. The combined effects of atmospheric circulation, radiative forcing, and water vapor transport anomalies lead to decreased precipitation across northern Europe and central Eurasia, with higher precipitation anomalies over northeast Asia. Finally, numerical experiments verify that the summer NAT excites atmospheric teleconnections that propagate downstream, affecting precipitation anomalies in mid–high-latitude Eurasia. This study provides a scientific basis for predicting Eurasian summer precipitation and strengthening disaster management strategies.

The Integration of Structural and Non-Structural Mitigation Based on Collaborative Governance Process at Tanjung Mas Sub-District

Abstract Subsidence of land levels and rising sea levels are two impacts of climate change that influence human activities, causing tidal floods. 12th and 16th RW (neighbourhood) of Tanjung Mas Subdistrict is a coastal area with a high population density that experiences tidal floods yearly. Structural mitigation programs receive more attention than non-structural programs. The mitigation process requires collaboration between stakeholders to obtain optimal results. This research aims to develop a disaster collaborative governance framework that integrates structural and non-structural mitigation. The research method used is a mixed method with a quantitative primary approach. The results show that collaborative governance in disaster mitigation involves various stages, such as collaborative initiatives, commitment to the process, collaborative processes, and monitoring evaluation. Non-structural mitigation is the primary basis for the success of the disaster management stage, while structural mitigation plays a significant role in the recovery stage. Emphasizing the balance of the role of the hexa helix is needed to create a sustainable mitigation process.

Machine learning and deep learning-based landslide susceptibility mapping using geospatial techniques in Wayanad, Kerala state, India

Landslide susceptibility mapping is vital for disaster management and sustainable land-use planning. This research was conducted in Wayanad, Kerala, India, to identify landslide susceptible zones. The study used large geospatial datasets, such as elevation, slope, aspect, curvature, stream power index, topographic wetness index, land use and land cover, rainfall, flow accumulation, geology, and geomorphology. It is followed by the application of various machine learning and deep learning models such as the support vector machine, artificial neural networks, logistic regression, random forest, gradient boosting machine, recurrent neural networks long short-term memory, and deep neural network models to map the landslide susceptible zones. The model was trained and validated using the landslide inventory map, which contains 298 sites of landslides. The random forest model, with 97 % accuracy, performed best. It is possible to effectively mitigate landslides and plan long-term land use by identifying hazardous zones within the study region.

Landslide risk assessment in mining areas using hybrid machine learning methods under fuzzy environment

Landslide risk assessment in coal mining economic cities faces challenges due to inadequate knowledge of indicator systems and modeling techniques, despite its crucial significance in geological disaster management. This study addresses this gap by proposing a novel framework encompassing data preparation, susceptibility analysis, consequence analysis, and final risk assessment. The primary objective is to reduce the complexity and inherent uncertainties associated with landslide risk assessment in these specific urban environments. First, two comprehensive databases for the indication system were established. One database focuses on landslide susceptibility, considering the region’s mining disturbances and disaster-prone environments. The other evaluates the effect of consequences while accounting for the pertinent risk variables. Second, a hybrid machine learning model called Geo-FR-SVM was developed by combining the GeoDetector, Frequency Ratio method, and Support Vector Machine model to improve landslide susceptibility mapping. Integrating the susceptibility map with the consequence map created using fuzzy set theory and fuzzy analytical hierarchy process methodologies resulted in the final landslide risk map. Our analysis revealed that elevation, distance to rivers and roads, groundwater level, and coal mining subsidence were the key factors influencing landslide occurrence in the Xishan mine area. The majority of the study area exhibits low landslide risk. However, higher consequence zones were primarily concentrated in highly populated residential regions and coal mine industrial zones in the east-central and western urban areas. Additionally, they are mainly distributed along the roads in the center region. With comparatively large percentages of landslide high-risk and extremely high-risk regions (4.596%, 3.855%, 3.625%, and 3.188%, respectively) at Tunlan Mine, Xiqu Mine, Zhenchengdi Mine, and Baijiazhuang Mine, these findings highlight the need for these mines to prioritize potential landslide identification and risk mitigation strategies. This framework offers broad applicability to other coal mining economic cities, serving as a valuable foundation for landslide risk management decision-making.

Shear mechanical behavior and fracturing path of red sandstone treated by joninted effect of water-fractures

Water content and primary fractures can change the mechanical characteristics of rock, making it easy to induce geological disasters. Therefore, direct shear tests of red sandstone under the action of water-fracture were carried out in this paper. The results show that shear strength of rock samples with fractures is less than that of intact rock samples. With the increase of primary fracture dip angle, shear strength and macroscopic crushing area of the rock sample increases first and then decreases with 20° as the boundary. It shows that the primary fractures weaken the shear mechanical properties and change the macroscopic failure mode. The shear performance of water-bearing rock samples is weaker than that of intact rock samples, and the weakening degree of water-saturated on shear performance of rock samples is lower than that of unsaturated water state. The fracture surfaces of rock samples are divided into 'shortest path single through type', 'longest path single through type' and 'cross path through type'. The failured rock samples are divided into 'single through type' and 'cross through type'. The research results can provide reference for geological disaster management under relevant conditions.

Utilization Of Artificial Intelligence And Big Data In Disaster Management: Future Risk Reduction Strategies

Disasters are events that significantly impact people's lives and living spaces globally. Natural disasters can arise from various causes, such as climate change, geological movements, weather events, and human factors. The damage caused by these disasters can affect millions of people and negatively impact societies economically, socially, and environmentally. Disaster management has emerged as a multidisciplinary field aimed at minimizing the damage caused by disasters and making communities more resilient to them. Traditional disaster management strategies include emergency planning, crisis management, pre-disaster preparation, and rapid response during disasters. However, these strategies generally reflect a reactive approach and rely on human resources and existing infrastructure. This article aims to examine the role and impact of innovative technologies such as artificial intelligence and big data in the field of disaster management. While artificial intelligence is known for its ability to analyze complex datasets, discover patterns and relationships, optimize decision-making processes, and predict future events, big data provides the ability to process large amounts of data quickly and efficiently, transforming them into meaningful information. These technologies play a significant role in pre-disaster preparation, crisis management during disasters, and post-disaster recovery processes. The article discusses how artificial intelligence and big data technologies can be used in disaster management, how these technologies can be integrated into disaster risk reduction strategies, and how their effectiveness can be assessed. In conclusion, the integration of artificial intelligence and big data technologies into disaster management offers a more effective and efficient approach to dealing with disasters and can make significant contributions to making communities more resilient to disasters. This article aims to provide a guide to understanding the current state of disaster management and developing more effective strategies.

Psychological preparedness in disaster management: A survey of leaders in Saudi Arabia's emergency operation centers

This study assesses perceptions of psychological preparedness among leaders in Emergency Operation Centers (EOCs) across Saudi Arabia. Effective disaster management requires managing stress, maintaining emotional stability, and adapting to rapid changes. A descriptive survey was conducted in 2024 with 110 leaders from various Health regions and the National Health EOC, achieving an 86.4 % response rate. Participants completed a structured questionnaire assessing their confidence in managing stress, the effectiveness of stress-relief techniques, and emotional stability. Results indicated high confidence in stress management (mean score 4.19, SD = 0.88) and emotional stability (mean score 4.27, SD = 0.80), along with moderately high ratings for the effectiveness of stress-relief techniques (mean score 3.64, SD = 0.99). Only 30.53 % had received formal training on psychological preparedness, while 96.84 % expressed a need for additional training. The study found significant correlations between confidence, emotional stability, and the regular review of coping strategies. These results highlight the critical need for comprehensive training programs to improve the psychological preparedness of disaster management personnel.

Disaster management training for environmental health: A narrative literature review.

The review suggests that with proper basic training for disaster responders, more lives can be saved during and after disasters. It highlights the insufficiency of current training programmes and emphasises the need for advanced role-specific training for environmental health practitioners. The review emphasises the need for advanced role-specific training, community assessment skills and focused disaster response strategies to enhance environmental health practitioners' ability to respond to disasters and improve public health resilience. Enhanced training, capacity building and collaboration are necessary to improve the competencies, skills and knowledge of environmental health practitioners in disaster risk management and public health emergencies.

Analysis of Police Disaster Management Activities After the Implementation of the Autonomous Police System: Focusing on Media Reports

Analysis of Police Disaster Management Activities After the Implementation of the Autonomous Police System: Focusing on Media Reports