Progress in Disaster Science最新文献

{"title":"Real-time decision support model for logistics of emergency patient transfers from hospitals via an integrated optimisation and machine learning approach","authors":"Maziar Yazdani,&nbsp;Siroos Shahriari,&nbsp;Milad Haghani","doi":"10.1016/j.pdisas.2024.100397","DOIUrl":"10.1016/j.pdisas.2024.100397","url":null,"abstract":"<div><div>During catastrophic events like natural disasters, pandemics, large-scale industrial accidents, or wars, hospitals must continue providing uninterrupted healthcare services despite significant challenges. However, they might also become victims of the disaster and face the necessity of evacuation. Existing hospital evacuation models, which primarily depend on essential data being available before evacuation, often fail to account for the dynamic nature of emergencies and oversimplify the complexities of real-world situations. This paper marks a paradigm shift towards a real-time, data-driven decision-support model for managing hospital evacuations during acute emergencies. The proposed model integrates data on factors such as the severity of the situation, resource status, patient needs, and road conditions. It employs a Bayesian ARIMA component to predict patient arrivals, specially tailored for limited sample sizes. A case study of a hypothetical flood emergency in the Hawkesbury-Nepean Rivers region in Western Sydney, Australia, demonstrates the advantages of a proposed framework equipped with predictive analytics compared to a purely optimization-based model. Numerical testing reveals that without a forward-looking component to predict patient transfer demand over future periods, there can be a misallocation of resources in the initial stages, leading to shortages of critical resources later in the emergency operation. The proposed dynamic decision support framework underlines the potential value of predictive analytics for anticipating future trends in disaster management and response. The findings offer potential advancements in understanding how data and technology can be harnessed to improve emergency responses, promoting more resilient healthcare systems.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"25 ","pages":"Article 100397"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Southern Iranian households preparedness in disasters and relationship with demographic factors","authors":"Aman Allah Zamani ,&nbsp;Abdoljabbar Zakeri ,&nbsp;Shokrollah Mohseni ,&nbsp;Gholamali Javdan ,&nbsp;Ali Azarpeikan ,&nbsp;Fatemeh Azadi ,&nbsp;Hassan Morshedi ,&nbsp;Vajihe Shamsaei ,&nbsp;Akram Ahmadizadeh Fini","doi":"10.1016/j.pdisas.2024.100401","DOIUrl":"10.1016/j.pdisas.2024.100401","url":null,"abstract":"<div><div>No community is immune to the disaster hazards in right now excessively connected globe. Multiple casualties in emergencies can be hard to control. Insufficient practice in reply to these occurrences can cause damage in periods of life and well-being, belongings, and foundation. The purpose of the study is to determine the level of preparedness of households in Hormozgan province facing disasters in 2022 to improve people's preparedness. Data were collected using the Household Disaster Preparedness Index (HDPI), including 15 preparedness measures. The questionnaire was completed by trained health system experts. 190,726 households, were participated in the study. The level of preparedness of households in Hormozgan was 39.54 %, the education index of households was 29.42 % and the number of evaluated households in the province was 33.22 %. The study results showed that participants aged 20 to 40, living in the city, and having a university education had the highest level of preparedness. Therefore, by implementing programs for people under 20, living in the villages, and having a diploma or less education, the level of readiness of the community against disasters can be increased. Furthermore, having an earthquake risk experience in Hormozgan province has increased the preparedness for activities related to this study.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"25 ","pages":"Article 100401"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lessons from the 2024 Noto Peninsula Earthquake: Need for digital transformation in disaster response","authors":"Sakiko Kanbara ,&nbsp;Rajib Shaw ,&nbsp;Kiyotaka Eguchi ,&nbsp;Sangita Das","doi":"10.1016/j.pdisas.2024.100400","DOIUrl":"10.1016/j.pdisas.2024.100400","url":null,"abstract":"<div><div>This paper explores the critical role of digital transformation (DX) in preventing secondary deaths and improving healthcare after the 2024 Noto Peninsula earthquake. As Japan grapples with an aging population, particularly in rural areas like Ishikawa Prefecture, the earthquake highlighted the vulnerabilities of elderly residents. The disaster's impact was exacerbated by misinformation and a digital divide, underscoring the need for robust digital infrastructure. Japan's digital transformation initiatives aim to bridge these gaps. This paper emphasizes the importance of DX in healthcare, advocating for real-time health monitoring, AI-driven for anticipatory action, and digital platforms for resource coordination. These tools are vital for timely medical interventions and preventing secondary deaths among vulnerable populations, especially during prolonged evacuations and in cold weather conditions. The paper highlights the need for adaptive governance and local community partnerships to ensure the effective use of digital technologies in disaster response and healthcare, ultimately enhancing resilience and disaster risk reduction.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"25 ","pages":"Article 100400"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approaches for seismic vulnerability assessment of urban buildings: A comparative study with analytic hierarchy process","authors":"Danesh Asadollahzadeh,&nbsp;Behrouz Behnam","doi":"10.1016/j.pdisas.2024.100398","DOIUrl":"10.1016/j.pdisas.2024.100398","url":null,"abstract":"<div><div>Implementing pre-disaster earthquake strategies is essential for minimizing post-earthquake impacts. In this vein, one key strategy is to assess the seismic vulnerability of existing urban buildings, enabling the adoption of necessary rehabilitation procedures. Here, important parameters influencing the seismic vulnerability of urban buildings are first documented and prioritized then using multi-criteria decision-making tools. This results in a vulnerability index (VI) representing the potential earthquake damage. Using semi-supervised machine learning (ML) methods, the corresponding VI is determined, and the results are compared with different methods. Various ML-based methods are analyzed for the available dataset to identify the most effective approach for this study. This methodology is then applied to an urban region to assess the VI not only for the current year (i.e., 2024) but also to predict it for 2044 and 2064. The VI of buildings indicates that approximately 60 % and 90 % of the structures in the studied region will experience significant damage to earthquakes in the years 2044 and 2064, respectively. In the final step, various ML methods are evaluated for data classification. Decision tree and random forest methods achieve an accuracy of over 95 %, while linear regression is utilized for predicting the index value, resulting in an R-squared error rate of approximately 91 %.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"25 ","pages":"Article 100398"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-income households' willingness to pay for flood risk insurance in South Africa","authors":"David Lefutso ,&nbsp;Abiodun A. Ogundeji ,&nbsp;Gideon Danso-Abbeam ,&nbsp;Yong S. Nyam","doi":"10.1016/j.pdisas.2024.100403","DOIUrl":"10.1016/j.pdisas.2024.100403","url":null,"abstract":"<div><div>As climate change leads to increase flood risks, South Africa continues to rely on government driven <em>ex post</em> relief initiatives for flood management whilst commercial insurance providers are not yet incorporated into broader flood management strategies. The willingness to pay (WTP) for flood risk insurance is investigated among this most vulnerable demographic of low income households. Using discrete choice experiments (DCE) and mixed logit models, it uses primary data from respondents in Buffalo City metropolitan municipality to analyse preferences for insurance attributes, such as coverage levels, premiums and excess fees. The findings also show that there is a strong preference for lower premiums, better quality insurers, and easier application processes for adoption. The results of mixed logit show that attributes like the increased building coverage results in positive WTP and further confirms the need for insurance plans that are easily accessible and affordable. Taken together, the findings in this research highlight the value of trust, transparency, and the cost effectiveness of policy design in boosting both consumption of flood insurance and community resilience to floods among vulnerable populations.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"25 ","pages":"Article 100403"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated framework for satellite-based flood mapping and socioeconomic risk analysis: A case of Thailand","authors":"Nutchapon Prasertsoong ,&nbsp;Nattapong Puttanapong","doi":"10.1016/j.pdisas.2024.100393","DOIUrl":"10.1016/j.pdisas.2024.100393","url":null,"abstract":"<div><div>This study introduces a novel approach to monitoring floods and estimating socioeconomic impacts in Thailand. The approach leverages advancements in geospatial data, employing two web-based applications developed on the Google Earth Engine platform. These tools provide user-friendly access to a vast array of satellite data at the provincial level, including flooded areas, nighttime-light density, drought index, rainfall, cropland, and urban areas. The study also merges these satellite-based indices with official provincial GDP data from 2018 to 2022 to empirically analyze socioeconomic impacts using four machine learning algorithms. The result obtained from Random Forest (RF) demonstrates the highest predictive power for GDP forecasting (r-squared value of 0.912). Feature analysis methods identified the proportion of flooded urban areas as one of the most significant variables in predicting provincial GDP. The RF prediction model was also employed to conduct counterfactual simulations for the period 2018–2022, hypothesizing a scenario devoid of flood events. This approach facilitated the determination of a theoretical GDP value in the absence of floods, thereby enabling the calculation of flood-related economic losses, which averaged 0.945 % of GDP. The study's analytical framework, notable for its cost-effectiveness, leverages openly accessible data and open-source software packages, making it highly applicable to various developing countries.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"25 ","pages":"Article 100393"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving operational use of post-disaster damage assessment for Urban Search and Rescue by integrated graph-based multimodal remote sensing data analysis","authors":"Sivasakthy Selvakumaran ,&nbsp;Iain Rolland ,&nbsp;Luke Cullen ,&nbsp;Rob Davis ,&nbsp;Joshua Macabuag ,&nbsp;Charbel Abou Chakra ,&nbsp;Nanor Karageozian ,&nbsp;Amir Gilani ,&nbsp;Christian Geiβ ,&nbsp;Miguel Bravo-Haro ,&nbsp;Andrea Marinoni","doi":"10.1016/j.pdisas.2025.100404","DOIUrl":"10.1016/j.pdisas.2025.100404","url":null,"abstract":"<div><div>This work investigates the application of remote sensing technologies within the specific operational context of emergency urban search and rescue (USAR) efforts post-disaster. We thoroughly investigate two innovative methodologies, each tailored to meet distinct operational goals in a USAR setting. The first employs a belief propagation framework that is designed to provide prompt and robust initial damage assessments using sparse data, with the capability to incorporate additional on-site information as it becomes available. The second methodology introduces a modified graph convolutional network to quantify the uncertainty levels inherent in damage classification tasks. Three case studies were considered, using damage assessment data from the 2020 Beirut explosion, the 2021 Haiti earthquake and the 2023 Türkiye-Syria earthquake. Our experimental results demonstrate the potential of these approaches to achieve operational objectives, particularly in terms of robustness and scalability in disaster scenarios. The versatility offered by graph-based methodologies establishes a solid foundation for addressing these dynamic challenges, suggesting a promising direction for continued research in this field.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"25 ","pages":"Article 100404"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of natural disasters and COVID-19 pandemic complex impacts on distribution of PPP loans","authors":"Azin Al Kajbaf ,&nbsp;Christina Gore ,&nbsp;Jennifer F. Helgeson ,&nbsp;Jarrod Loerzel","doi":"10.1016/j.pdisas.2024.100395","DOIUrl":"10.1016/j.pdisas.2024.100395","url":null,"abstract":"<div><div>Paycheck protection program (PPP) loans were established during the COVID-19 pandemic to help U.S-based businesses continue paying their employees. PPP loans were meant to help businesses recover from the disruptions caused by the COVID-19 pandemic; however, pre-existing socioeconomic stressors and the impacts of concurrent or previous climate and weather disasters could also amplify the impacts experienced by businesses. The concurrence of these interrupting acute shocks and chronic stressors creates a complex event that can result from combinations of natural, biological, and human-made causes. It is recognized that complex events tend to create an impact that is greater than the sum of their parts. The objective of this study is to evaluate whether the PPP loan allocations are correlated with prior community (i.e., county-level) experience of climate and weather disasters. This analysis seeks to understand whether past experience improves or diminishes the ability of businesses, and the communities in which they function, to respond to disruptive events. The heterogeneity in the correlations is investigated by examining climate and weather disaster occurrence across time, severity of impacts, and county-level characteristics. Our analysis results show a strong association between counties that previously experienced natural hazard events and the first wave of PPP loans from April 3rd to April 16th, 2020; however, the direction of association is different based on the extent of experience. Furthermore, counties that had increased levels of economic risk, including measures of community resilience and relatively greater unemployment rates, received less PPP loan allocations. We believe that the results of this study can potentially be helpful in decision-making regarding the allocation of recovery grants in future and start an important conversation about the structure of support offered to business during and following disaster events.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"25 ","pages":"Article 100395"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lightning-induced vulnerability assessment in Bangladesh using machine learning and GIS-based approach","authors":"Tanmoy Mazumder,&nbsp;Md. Mustafa Saroar","doi":"10.1016/j.pdisas.2025.100406","DOIUrl":"10.1016/j.pdisas.2025.100406","url":null,"abstract":"<div><div>This study investigates the lightning-induced vulnerability in Bangladesh using Geographic Information Systems (GIS) and Machine Learning (ML) techniques, addressing the limited research in this area. Lightning, especially prevalent during April to June, is a significant threat in Bangladesh, causing fatalities, injuries, and economic losses. By analyzing spatiotemporal patterns of lightning and casualties, and incorporating meteorological, geographical, and socio-economic factors into ML models (Random Forest, Multinomial Logistic Regression, Support Vector Machine, and Artificial Neural Networks), this research provides a nuanced understanding of lightning impacts. Findings indicate a downward trend in lightning strikes but not necessarily in fatalities, revealing the complexity of contributing factors. Northern Bangladesh experiences more lightning strikes, whereas the northeast has higher casualty rates. Correlation analysis indicates that lightning fatalities are influenced by multiple factors, with high correlations to cropland area (0.69), agricultural population (0.61), and lightning flashes (0.45). The Random Forest model has appeared to be the best model to predict [with high accuracy] the influence of lightning vulnerability factors. The most significant predictors of lightning vulnerability are cropland area (32 %) followed by literacy rate (19 %), rural population (18 %), lightning flashes (16 %), and water area (15 %) in Bangladesh. The extensive presence of croplands and rural populations increases exposure to lightning during peak farming seasons, while low literacy rates exacerbate risks by limiting awareness of safety measures. Additionally, large water bodies influence local microclimates and pose risks to those working or travelling in and around these wetlands such as agriculture laborers and fishermen. Changes in lightning flash frequencies due to climate variability, combined with socio-economic disparities and infrastructure deficits, further amplify vulnerabilities. A district-level vulnerability map developed in this study provides actionable insights for geographically/area-based targeted policy interventions to address these interlinked factors driving vulnerability. This comprehensive, data-driven approach marks a significant advancement in our understanding of lightening vulnerability and offrrs valuable insight for strategy developed to combat the fatalities of lightning in Bangladesh.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"25 ","pages":"Article 100406"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resilience rising: Redefining livelihood systems in disaster-prone rural communities","authors":"Reza Amarta Prayoga ,&nbsp;Eko Wahyono ,&nbsp;Nuzul Solekhah ,&nbsp;Fatwa Nurul Hakim ,&nbsp;Siti Fatimah ,&nbsp;Lis Purbandini ,&nbsp;Djoko Puguh Wibowo ,&nbsp;Rachmini Saparita","doi":"10.1016/j.pdisas.2024.100391","DOIUrl":"10.1016/j.pdisas.2024.100391","url":null,"abstract":"<div><div>This study emphasizes the need for a critical review of existing literature to identify the enablers and barriers to social modeling. Rather than solely focusing on vulnerability, it seeks to deconstruct and redefine resilience, particularly in the context of livelihood systems within communities that have been underexplored in current research. Through a qualitative approach, the study combines critical and constructivist paradigms to develop social modeling that enhances the resilience of disaster-prone communities via their livelihood systems. The goal is to create an innovative, participatory, and sustainable model for rural community livelihoods that can withstand challenges. Central to this model is the accumulation of both capital and social capital. The study offers strategic and practical recommendations for stakeholders and communities in disaster-prone areas to rebuild more robust livelihood systems by harnessing ecological, social, economic, and cultural potentials. It has significant implications for the analytical framework of community livelihood systems and the strategic and operational planning needed to address livelihoods in disaster-affected areas. Social modeling is a critical strategy for planning and implementing social protection and economic mitigation in such communities.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"24 ","pages":"Article 100391"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}