Progress in Disaster Science最新文献

{"title":"Assessing preference heterogeneity for nature-based flood control measures: An econometric analysis using a choice experiment","authors":"Takahiro Tsuge ,&nbsp;Ayumi Onuma ,&nbsp;Jun Nishihiro ,&nbsp;Yuki Shibata ,&nbsp;Taro Shoji ,&nbsp;Yasutaka Satake ,&nbsp;Takehito Yoshida","doi":"10.1016/j.pdisas.2025.100444","DOIUrl":"10.1016/j.pdisas.2025.100444","url":null,"abstract":"<div><div>In recent years, green infrastructure has gained attention due to the increasing risk of disasters caused by extreme weather and growing concern for environmental conservation. A key feature of green infrastructure is its multifunctionality. To accurately assess its benefits, it is necessary to evaluate the full range of its functions. This study employed a choice experiment to estimate the value of various aspects of nature-based flood control measures in the Lake Inba basin in Japan. The results, derived using a random parameter logit model, revealed that (1) certain flood countermeasures were highly preferred, (2) respondents' willingness to pay (WTP) for a 1 % reduction in the annual probability of flood damage was 771 JPY, (3) marginal utility diminished as the reduction in flood damages within local communities increased, and (4) environmental benefits of green infrastructure were significant. Additionally, results from the latent class model identified two respondent groups with distinct preferences. The group that was more concerned about flooding was more proactive in supporting countermeasures and had higher WTP for each function that nature-based countermeasures provide than the other group. These indicate that green infrastructure not only provides critical flood control benefits but also offers significant environmental and recreational advantages.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"27 ","pages":"Article 100444"},"PeriodicalIF":2.6,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drought vulnerability assessment and mitigation strategies for peri-urban province of Pathum Thani, Thailand","authors":"Panita Saguansap ,&nbsp;Prinya Mruksirisuk ,&nbsp;Duangporn Garshasbi ,&nbsp;Nawhath Thanwiset Thanvisitthpon","doi":"10.1016/j.pdisas.2025.100431","DOIUrl":"10.1016/j.pdisas.2025.100431","url":null,"abstract":"<div><div>This study assesses the drought vulnerability of Thailand's peri-urban province of Pathum Thani using a three-component vulnerability assessment framework, comprising drought exposure, drought sensitivity, and drought adaptive capacity components. Pathum Thani province, consisting of seven administrative districts, is home to a number of industries including agriculture, manufacturing, and tourism. Rapid urbanization and climate change have exacerbated the province's drought situations. To assess the drought vulnerability of Pathum Thani, drought vulnerability indicators structured around the three vulnerability components are developed across the three sustainability dimensions: social, economic, and environmental dimensions. The drought vulnerability indicators are initially evaluated by experts for their relevancy. The drought indicators are further evaluated using a questionnaire administered to randomly selected households across seven administrative districts. The drought vulnerability components and indicators, based on the questionnaire responses, are subsequently validated by using structural equation modeling and confirmatory factor analysis. After the validation, a drought vulnerability questionnaire is developed to evaluate the drought vulnerability of the study area, measured by the province- and district-level drought vulnerability indexes. The research findings reveal a moderate level of drought vulnerability across most administrative districts. As a result, policymakers should focus interventions and mitigation strategies on reducing drought exposure, cultivating drought resilience, and enhancing adaptive capacity.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"26 ","pages":"Article 100431"},"PeriodicalIF":2.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble weather-runoff forecasting models for reliable flood early warning systems","authors":"Alberto de la Fuente ,&nbsp;Carolina Meruane ,&nbsp;Viviana Meruane","doi":"10.1016/j.pdisas.2025.100420","DOIUrl":"10.1016/j.pdisas.2025.100420","url":null,"abstract":"<div><div>Flood early warning systems often rely on a single hydro-meteorological forecast, which can limit reliability. Recent advances in deep learning (DL) offer promising improvements due to their low computational cost, enabling the generation of ensemble forecasts. This study investigates how to process multiple weather-runoff forecasts to improve model performance in predicting extreme events. We applied DL-based weather-runoff forecasting in river stations located at the foot of the Andes Mountains in Chile. The models couple a near-future global weather forecast with short-range runoff forecasting systems based on Long Short-Term Memory (LSTM) cells. Meteorological and geomorphological input variables commonly used in hydrological models were selected. Training and validation used ERA5 data, while NCEP-GFS data were used for testing and real-time operation. Model performance was evaluated using the Kling-Gupta efficiency (0.6–0.8) and Nash-Sutcliffe efficiency (greater than 0.9). The threat score index, which assesses the model's ability to predict threat peak flow exceedance, ranged between 0.6 and 0.8. The best-performing models were analyzed probabilistically to quantify uncertainty. Finally, we introduced the concept of conditional probability to estimate the likelihood of exceeding a threat peak flow, providing a basis for raising alerts and improving decision-making under uncertain conditions.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"26 ","pages":"Article 100420"},"PeriodicalIF":2.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791378","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":"Understanding the dynamics of evacuation delays: A study of the 2021 mount Semeru eruption through PLS-SEM analysis","authors":"I Dewa Made Frendika Septanaya ,&nbsp;Adjie Pamungkas ,&nbsp;Anoraga Jatayu ,&nbsp;Rivan Aji Wahyu Dyan Syafitri ,&nbsp;Amien Widodo ,&nbsp;Mayra Andrakayana","doi":"10.1016/j.pdisas.2025.100433","DOIUrl":"10.1016/j.pdisas.2025.100433","url":null,"abstract":"<div><div>This study explores the key factors contributing to evacuation delays during the 2021 eruption of Mount Semeru in Indonesia. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), data were collected from 100 affected residents to examine behavioural responses and decision-making dynamics during the crisis. The analysis tested eight hypotheses and found that five were statistically significant, indicating that lack of information, emotional attachment to property, absence of evacuation plans, limited infrastructure, and family-related concerns were positively associated with delayed evacuation decisions. Notably, 71 % of respondents relied on neighbours as their primary information source, and 80 % reported experiencing panic during the evacuation process. These findings highlight critical gaps in community preparedness and emergency communication systems. The study concludes that strengthening early warning dissemination, enhancing infrastructure, conducting regular evacuation drills, and addressing socio-emotional factors are essential to improving evacuation effectiveness. This research contributes to a deeper understanding of evacuation behaviour during volcanic disasters and offers practical recommendations to enhance community resilience and emergency management practices.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"26 ","pages":"Article 100433"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disaster warning messages: challenges and opportunities based on Brazil's experience","authors":"Murilo Noli da Fonseca,&nbsp;Luciene Pimentel da Silva","doi":"10.1016/j.pdisas.2025.100440","DOIUrl":"10.1016/j.pdisas.2025.100440","url":null,"abstract":"<div><div>This research investigates the effectiveness of disaster communication messages in Brazil, with a focus on the structure and content of SMS messages sent out from 2018 to 2023. The Warning Response Model was used for coding. The analysis reveals that only 1.83 % of the 73,701 messages analyzed were complete; that is, they contain all the elements to be effective (source, hazard, location, guidance and time). The results also show that messages about natural hazards achieved the highest scores and that the states of Santa Catarina and Paraná stand out. In contrast, states such as Minas Gerais and Goiás achieved low scores regarding messages about technological risks. In addition, lacking geographical and time specifics, and failing to include details on the potential impacts compromise the effectiveness of the messages. The study highlights that the message length limit of 160 characters is an obstacle to effective communication, which can compromise the population's perception of risk, its adoption of protective actions. The research concludes that optimizing disaster communication messages is crucial to improve disaster response in Brazil, while it recommends using a multichannel system, the continuous training of civil defense agents, and greater community engagement to strengthen resilience.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"26 ","pages":"Article 100440"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On moving towards a more inclusive understanding of disaster risk reduction: A sexual and gender minorities perspective through the lens of global flood risk","authors":"Eric Mortensen ,&nbsp;Ana Clara Cassanti ,&nbsp;Timothy Tiggeloven ,&nbsp;Anne Twaalfhoven ,&nbsp;Philip J. Ward ,&nbsp;Toon Haer","doi":"10.1016/j.pdisas.2025.100442","DOIUrl":"10.1016/j.pdisas.2025.100442","url":null,"abstract":"<div><div>Considering social vulnerability of marginalized communities is crucial for equitable disaster risk reduction. This paper emphasizes the need to include sexual and gender minorities in global vulnerability assessments and policymaking. This community faces unique challenges in disasters, often overlooked in disaster risk reduction strategies and agendas. While natural hazards do not discriminate, societal ideologies and laws can, amplifying disparate outcomes. Our flood risk analysis reveals that over one-third of the global expected annual affected population – 26.5 to 33.9 million people – live in countries lacking legal protections for sexual and gender minorities. Future scenarios indicate this could double to 58.6 to 73.1 million by 2050. Meanwhile, two-thirds of those at risk to floods reside in countries with below-average societal acceptance of sexual and gender minorities, increasing their vulnerability before, during and after flooding disasters. To address these disparities, global frameworks must urgently integrate specific metrics into social vulnerability assessments and risk planning. Including marginalized communities ensures that disaster risk reduction efforts are more responsive and effective. By acknowledging the intersection of societal acceptance, legal protections, and disaster risk, we can advance more inclusive and impactful strategies to mitigate the growing impacts of climate-fuelled hazards like coastal and riverine flooding.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"26 ","pages":"Article 100442"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flood prediction in urban areas based on machine learning considering the statistical characteristics of rainfall","authors":"Se-Dong Jang,&nbsp;Jae-Hwan Yoo,&nbsp;Yeon-Su Lee,&nbsp;Byunghyun Kim","doi":"10.1016/j.pdisas.2025.100415","DOIUrl":"10.1016/j.pdisas.2025.100415","url":null,"abstract":"<div><div>Urbanization has increased impervious surfaces, while climate change has intensified rainfall, leading to more frequent urban flooding. Traditional numerical models for flood prediction are accurate but time-consuming due to extensive parameter calibration and data processing. This study addresses these limitations by proposing a machine learning-based flood prediction method using a Random Forest model. By utilizing past rainfall data, 1D drainage system simulations, and 2D flood analyses, we trained the model to predict flood patterns for various rainfall events. To enhance prediction accuracy, statistical characteristics of rainfall, such as temporal distribution, were incorporated into the model. Performance metrics (RMSE, R², MAE) for the test dataset showed values of 3.1573, 0.9682, and 0.9484 for the total rainfall model, and 2.7354, 0.9761, and 0.8942 for the model with statistical characteristics. Both models displayed high predictive accuracy relative to the numerical model, with the Random Forest model using statistical characteristics showing slightly improved performance. This method provides faster, reliable flood predictions, offering a valuable tool for real-time urban flood management and decision-making during emergency situations.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"26 ","pages":"Article 100415"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791379","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":"Ground robot technologies in wildfire risk reduction. The viewpoint of the fire service","authors":"Pawel Gromek ,&nbsp;Thomas Lowe","doi":"10.1016/j.pdisas.2025.100435","DOIUrl":"10.1016/j.pdisas.2025.100435","url":null,"abstract":"<div><h3>Background</h3><div>Robots are not widely used in wildfire risk reduction. Firefighters do not commonly know how to use them and technology providers are not aware of key operational directions for improvements.</div></div><div><h3>Objective</h3><div>This study aims to identify, catalogue and discuss directions for the development of robot technologies in terms of wildfire risk reduction. The viewpoint of the fire service is presented.</div></div><div><h3>Method</h3><div>Survey was conducted with experts to gather new knowledge on the use of robots in wildfire response and to identify inspiration for improvements for technology providers.</div></div><div><h3>Results</h3><div>92 end-user-related developments were categorised into particular elements of wildfire response process. 31 development directions related to technology providers have been assigned to general robot functionalities: ensuring safety of firefighters, shaping situational awareness, and supporting firefighting systems. The robot functionality sets can be implemented in reconnaissance robots, delivery and evacuation robots, and firefighting robots.</div></div><div><h3>Conclusion</h3><div>Fire service perceives the robot use in wildfire risk reduction more broadly than is reflected by currently developed disaster robots and existing disaster risk reduction concepts. The viewpoint of the fire service can raise awareness among end-users and inspire technology providers to effectively and rationally implement robots for wildfire risk reduction.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"26 ","pages":"Article 100435"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based identification and assessment of snow disaster risks using multi-source data: Insights from Fukui prefecture, Japan","authors":"Zhenyu Yang ,&nbsp;Hideomi Gokon ,&nbsp;Qing Yu","doi":"10.1016/j.pdisas.2025.100426","DOIUrl":"10.1016/j.pdisas.2025.100426","url":null,"abstract":"<div><div>Understanding the driving factors behind snowstorm risk and their nonlinear effects is critical for developing effective response strategies. This study, focusing on the 2018 Fukui snowstorm in Japan, integrates multi-source data, including mobile GPS data, Digital Elevation Model (DEM) data, road data, urban data, and traffic congestion data, to develop an interpretable model for quantifying high-risk areas and examining key nonlinear relationships and threshold effects influencing snowstorm impact occurrence, offering actionable insights for mitigation strategies. We employed four machine learning models—Decision Tree, Random Forest, Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost)—to capture complex nonlinear relationships among influencing factors and applied SHAP (SHapley Additive exPlanations) theory to interpret variable contributions. The results reveal that: (1) compared to Random Forest, Decision Tree, and MLP models, the XGBoost model demonstrates superior performance with a prediction accuracy of 0.8225; (2) factors such as elevation, slope, road density, and road width exhibit significant nonlinear impacts and threshold effects on snowstorm impact occurrence; (3) Urban areas with elevation below <span><math><mn>51.9</mn><mspace></mspace><mi>m</mi></math></span>, slopes exceeding <span><math><msup><mn>9.9</mn><mo>°</mo></msup></math></span>, a density of major roads (Road Type 1) less than <span><math><mn>443.75</mn><mspace></mspace><mi>m</mi><mo>/</mo><msup><mi>km</mi><mn>2</mn></msup></math></span>, a density of minor roads (Road Type 2) less than <span><math><mn>550.25</mn><mspace></mspace><mi>m</mi><mo>/</mo><msup><mi>km</mi><mn>2</mn></msup></math></span>, and where rural roads (Road Type 3) are nearly absent, along with population fluctuations ranging between <span><math><mfenced><mrow><mo>−</mo><mn>0.25,0</mn></mrow></mfenced></math></span>, are particularly vulnerable to snow disasters. In contrast, areas with flat terrain and high densities of rural roads are less likely to be affected; and (4) snow disaster resilience in mitigating traffic congestion can be improved by monitoring GPS data for early warnings and optimizing the sp. configuration of major and minor roads.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"26 ","pages":"Article 100426"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building damage assessment in natural disasters: A trans- and interdisciplinary approach combining domain knowledge, 3D machine learning, and crowdsourcing","authors":"Julia Kohns ,&nbsp;Vivien Zahs ,&nbsp;Carolin Klonner ,&nbsp;Bernhard Höfle ,&nbsp;Lothar Stempniewski ,&nbsp;Alexander Stark","doi":"10.1016/j.pdisas.2025.100427","DOIUrl":"10.1016/j.pdisas.2025.100427","url":null,"abstract":"<div><div>Recent natural disasters have claimed many lives. Reliable damage predictions and timely assessments are essential for effective rescue operation planning and efficient allocation of limited resources. Currently, experts in the field perform damage assessment manually, which is resource- and time-intensive. To address this issue, we propose a general trans- and interdisciplinary concept that combines the strengths of domain knowledge, automated computational methods, and crowdsourcing. The objective is to provide relevant and timely damage information after a natural disaster. The specific implementation presented for the earthquake damage use case includes (1) the development of a set of novel, innovative methods, (2) their combination to obtain timely and reliable damage information, (3) fully defined interfaces between all components to ensure an automated data flow, (4) implementation as a fully open-source framework, and (5) the participation of end users in the development of the framework from the beginning, contributing their expertise. Compared to other existing individual solutions, our interdisciplinary implementation has shown to provide fast and accurate information in disaster situations, aiding the management of consequences and saving lives. We consider the implementation transferable to various types of natural hazards due to its open-source realisation and the flexibility of its modules and interfaces.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"26 ","pages":"Article 100427"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}