Disaster Management Research Articles

Flood Hazard Mapping in Cimandiri Watershed using Fuzzy Logic Method Based on Multi-Source Geospatial Datasets

Abstract Flood is a frequent disaster in the Cimandiri watershed. This is evidenced by the fact that during 2017-2022, 37 floods occurred in this area. This condition causes losses to the community and damage to the environmental ecosystem. Therefore, disaster management is needed to overcome and reduce the impact of this flood. One management that can be done is by mapping flood hazard areas. However, until now there is no map available that presents the flood hazard study area in the Cimandiri watershed. If available, the maps generally cover Regency, sub-Regency, or large-scale maps covering the entire territory of Indonesia. This condition causes deficiencies or misinformation on the map because the scale of the study is irrelevant. Based on this, this study attempts to conduct flood hazard mapping in the Cimandiri watershed with a scale of analysis of 100,000. This study uses fuzzy logic method to generate hazard maps based on spatial parameters, rainfall, distance from the river, elevation, slope, vegetation density, land cover, soil type, and topographic wetness. The results of this method produce a hazard map with a class range of 0 to 1, a class that is close to 1 means that it has a high level of hazard. To see the correctness of this map, a validation test was carried out using flood data from 2017-2022 which contained 37 flood event data. Based on the validation results, 86% of flood points are in the non-0 hazard class (low hazard), only 13% are incorrect because they are in the 0-hazard class.

Assessment of internal disaster and emergency preparedness in hospitals using the Hospital Safety Index tool

BackgroundWhile being vital infrastructure in circumstances of crisis, health and treatment facilities' equipment is at risk of destruction, and harm to healthcare structures may prevent medical services from being delivered. Enhancements could promote resilience, and assessing current facilities for internal disaster preparedness is crucial. An evaluation of three hospitals' internal emergency and disaster preparedness is presented in this paper. MethodsHospital safety and preparedness were assessed using the World Health Organization's (WHO) Second Edition of Hospital Safety Index (HSI) tool. The safety indices were determined by three modules: emergency and disaster management, non-structural, and structural. A safety rating, represented as a probability with a maximum value of one and a minimum value of zero, is determined by adding up the weighted results. Over three days, a multi-disciplinary team performed three hospital assessments through staff interviews and visual inspections. ResultsThe study hospitals' safety indices were 0.90, 0.60, and 0.73; two were classified as Classification A, meaning they will probably be able to function in emergencies and disasters, and one was classified as Classification B, meaning that short-term intervention measures would be required to ensure the hospital's proper operation in the event of an internal disaster. This study has identified areas that must be prepared for internal disasters to increase resilience. These areas include (a) infrastructure, which has weaker protection, access, and physical security due to relatively old buildings; (b) critical systems, which have emergency response committees established by hospitals and can be improved by hospital staff receiving emergency response training; and (c) non-structural issues, which include the need to improve fuel storage, cupboard safety, shelf contents, and fitting protection. ConclusionClassification A applies to hospitals 1 and 3, whereas Classification B applies to Hospital 2. The HSI tool is a user-friendly, all-inclusive instrument offered by a reputable organisation that should be frequently used to evaluate the internal preparation of different hospitals for disasters.

NSEMBLE FINE TUNED MULTI LAYER PERCEPTRON FOR PREDICTIVE ANALYSIS OF WEATHER PATTERNS AND RAINFALL FORECASTING FROM SATELLITE DATA

The accurate prediction of weather patterns and rainfall forecasting is critical for various sectors, including agriculture, disaster management, and water resource planning. Traditional models often struggle to capture the complex interactions between atmospheric variables, particularly when integrating diverse types of satellite data (binary, categorical, and numerical). To address this challenge, an ensemble fine-tuned multi-layer perceptron (MLP) model is developed, combining the strengths of multiple machine learning techniques for more robust predictions. The primary problem is the difficulty in handling mixed data types while maintaining high prediction accuracy. Satellite data, including binary indicators (e.g., cloud presence), categorical features (e.g., cloud types), and numerical variables (e.g., temperature, humidity, and wind speed), provide rich information but require specialized processing for effective forecasting. The proposed method involves fine-tuning an ensemble of MLP models with backpropagation, dropout regularization, and batch normalization to reduce overfitting and enhance generalization. The ensemble integrates predictions from individual MLP models, each trained on different subsets of features (binary, categorical, numerical). This technique allows the model to leverage complementary strengths and produce more accurate rainfall forecasts. Satellite data is preprocessed and normalized before training, and categorical variables are one-hot encoded to ensure compatibility with the MLP architecture. Results from testing on historical satellite weather datasets demonstrate significant improvements in forecast accuracy. The ensemble MLP achieved an accuracy of 91.3%, with a precision of 90.7%, recall of 89.5%, and an F1-score of 90.1%. The model performed exceptionally well in identifying critical rainfall events, reducing false positives by 12% compared to traditional models.

Elucidations of Present Situation of Preparedness for Natural Disasters at Home in Japan and its Effective Factors

This study elaborates on the measures to improve preparedness for natural disasters by elucidating the present situation of preparedness for natural disasters at home in Japan as well as the effective factors to promote such preparedness. An Internet monitoring survey was conducted throughout Japan, and valid responses (n=1,599) were obtained. To measure preparedness for natural disasters at home, we used 31 items, including the ones in the “public opinion poll on disaster management” conducted by the Cabinet Office, Government of Japan. The Internet monitoring survey revealed that approximately 30% of the respondents arranged for articles they may require in the event of a disaster and had contemplated their evacuation actions immediately after a disaster. The Internet monitoring survey results were compared with the results of the Cabinet Office, Government of Japan’s public opinion poll. It revealed that the internet monitoring survey results better represent the tendency of the Japanese people as a whole. A multiple regression analysis was conducted to determine six independent variables—age, marital status, prior disaster experience, awareness of hazard maps, disaster imminence, and communal exchanges—statistically significant as effective factors to improve preparedness for natural disasters at home. Particularly, considering a large standardizing coefficient (β) for awareness of hazard maps, it is proposed that the most effective measure to improve preparedness for natural disasters at home to comprehend the risks posed to one’s community and own house is by using the opportunities of disaster prevention education and drill rather than simply viewing the hazard maps aimlessly.

Expert perspectives on disaster risk reduction strategies in the tourist area of Borobudur-Yogyakarta-Prambanan in Indonesia

Tourism plays a crucial role in the economic growth of many countries. However, tourist destinations located in disaster-prone areas face increased vulnerability in the event of natural disasters. It is essential for these areas to have specific interventions, plans, and policies that focus on reducing disaster risk, while ensuring the safety of residents and tourists. This study proposes strategies for minimizing disaster risk using Indonesia's Borobudur-Yogyakarta-Prambanan (BYP) region as a case study. The study used an Analytical Hierarchial Process in a Strengths Weaknesses Opportunities and Threats (SWOT-AHP) analysis to prioritize factors through pairwise comparisons and develop strategic alternatives based on expert perspectives. Primary data were collected through aSWOT-AHP questionnaire that was provided to 22 experts in tourism and disaster management in Central Java and Yogyakarta Special Provinces. Secondary data were obtained from government documents, specifically the Disaster Risk Reduction Plan for the BYP regions (2023–2027). The results indicate that “implementing land-use policies to promote tourism development while considering disasters” is the most important strategy, based on expert judgment. The BYP tourist destinations provided a case study to identify strategies to reduce disaster risk in tourism areas.

The Mediating Role of Trauma Centrality in the Correlation Between Coping Self-Efficacy and Posttraumatic Growth Level of Disaster Volunteers.

Volunteers who respond to disasters play a crucial role in disaster management. However, their indirect exposure to traumatic events during their duties can adversely affect their mental health. The aim of the study was to determine the correlation among trauma centrality, coping self-efficacy, and posttraumatic growth and trauma centrality as possible mediator role in this relationship. A total of 112 disaster volunteers participated. The data were collected with a Demographic Form, the Centrality of Events Scale, the Post-Traumatic Growth Scale, and the Coping Self-Efficacy Scale. Disaster volunteers exhibited above average event centrality, high levels of posttraumatic growth, and coping self-efficacy. A positive correlation was identified among trauma centrality, posttraumatic growth, and coping self-efficacy, with trauma centrality playing a full mediating role. This study will be instrumental in better understanding the posttraumatic processes of disaster volunteers and in strengthening coping capacities, guiding the implementation of preventive and protective psychosocial interventions.

Research and Application of Emergency Logistics Resource Allocation Algorithm based on Supply Chain Network

The ”Fuzzy-Enhanced for Emergency Logistics Resource Allocation in Supply Chain Networks (FEM-ELRAS)” presents a novel approach to optimizing emergency logistics and resource allocation in supply chain networks, especially during critical disaster response scenarios. This research integrates fuzzy logic with the improve Multi Agent Genetic Algorithm (MAGA), creating a more adaptive and efficient framework capable of handling the uncertainties and complexities inherent in emergency situations. FEM-ELRAS employs fuzzy decision variables to represent ambiguous and fluctuating parameters like demand at disaster sites, supply availability, and variable transportation conditions. It incorporates a fuzzy inference system, utilizing expert-derived rules to guide the allocation process amidst uncertain and rapidly changing conditions. The algorithm’s evaluation mechanism is enhanced with fuzzy logic, offering a refined assessment of solution effectiveness, balancing multiple logistical objectives such as minimizing response time, optimizing costs, and maximizing resource utilization and delivery precision. Moreover, fuzzy logic principles are integrated into the genetic algorithm’s operators, enabling more context-sensitive and flexible solution adaptations. FEM-ELRAS is particularly designed to navigate the trade-offs between different logistical goals in emergency scenarios, making it a robust tool for decision-makers in disaster management. Its application promises significant improvements in emergency response efficiency, showcasing a step forward in the field of emergency logistics and supply chain management.

Disaster management of COVID-19: A study of India

COVID-19 capsulated the whole world in a dangerous loop of death and crippled the transformative capacity of science and technology to save the mankind. Law, once monopolized by man, was soon hijacked by the law of nature and the only remedy available was to draw boundaries (in form of lockdown). Legislative measures, medical facilities, and vaccine drives altogether helped in timely control of COVID-19; however, the world statistical data is evident of the fact that few countries were able to manage COVID-19 very well in contrast to others. It is true that no country was prepared for COVID-19 and neither was India, however, India objectified the interpretation of its existing legislations purposefully to control the spread of COVID-19. The objective of this article is to narrate the best practices adopted by India which helped in timely control of COVID-19 and finally suggest guidelines to control such pandemics in future. Keywords: COVID-19, Disaster Management, Drugs, Lockdown, Vaccines.

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.

Radiological perspective on earthquake trauma: differences in children and adults.

February 2023 saw major earthquakes in Pazarcık and Elbistan, causing significant devastation in Turkey. Patients were transferred to hospitals in neighboring provinces, with multiple traumas-especially fractures and organ injuries-forming the main reasons for hospital admissions. This study aimed to examine earthquake-related injuries in pediatric and adult populations to understand differences. This study analyzed 1,220 adults and 590 pediatric patients with radiological imaging out of 8,704 earthquake trauma cases. Radiological images were assessed independently by two radiologists. Statistical analysis using SPSS examined relationships between variables such as age group and injury type. Results showed 40% of adults and 64% of children had normal radiological findings. Cerebral and extremity traumas were most common in pediatrics, while adults showed more extremity, thoracic, and spinal traumas. Significant differences between adult and pediatric groups were observed in cranial fractures, thoracic and lumbar vertebral fractures, hemopneumothorax, lung contusions, rib fractures, femur and talocalcaneal fractures, and compartment syndrome (p<0.001). Earthquake-related injuries may vary between children and adults. Due to children's more flexible anatomical structure, it is believed that earthquake-related injuries occur less frequently in children. In this study, head traumas were more common in children compared to adults. The rate of cranial fractures was significantly higher in children, with a higher incidence of epidural hematoma compared to adults. Spinal traumas were more frequent in adults than in children, attributed to children's greater flexibility reducing the risk of entrapment under rubble. Pediatric thoracic compliance being significantly higher than in adults often resulted in milder chest traumas. However, compartment syndrome was more common in children, with a lower rate of accompanying bone fractures compared to adults. No significant difference was observed between children and adults in maxillofacial, abdominal, and pelvic traumas. These findings provide insights for future disaster healthcare planning and management.

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.

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 &gt; T) and unburned pixels (dNDVI &lt;= 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.