Pub Date : 2025-10-01Epub Date: 2025-06-26DOI: 10.1016/j.pdisas.2025.100446
Naomi Ito , Yurie Kobashi , Yuri Kinoshita , Nobuaki Moriyama , Toshiki Abe , Hiroaki Saito , Isamu Amir , Chika Yamamoto , Mika Sato , Masaharu Tsubokura
After the 2011 Great East Japan Earthquake, Soma City in Fukushima Prefecture, sustained extensive damage, and subsequently developed public housing for older victims, namely ‘Soma Idobata Nagaya’. The current interview survey aimed to report the living conditions of such older victims in Nagaya and accordingly suggest strategies for supporting the older population who experience disasters. We conducted semi-structured interviews with 32 Nagaya residents, who relocated there due to the earthquake, and performed a thematic analysis of the data. The development of Nagaya in Soma City after the earthquake enabled older residents to return to their familiar neighbourhoods and provided social security for those vulnerable to disasters. Beyond housing, Nagaya offered psychological stability and supported their independent living in a way they desired. It played a substantial role in rebuilding their lives, allowing them to overcome the hardships of the disaster and reintegrate into the local community. In summary, this study emphasizes the critical role of communities in post-disaster recovery and proposes new perspectives for supporting older adults in such contexts.
{"title":"Rebuilding lives in Nagaya, a public housing for older victims of the great East Japan earthquake: An interview survey","authors":"Naomi Ito , Yurie Kobashi , Yuri Kinoshita , Nobuaki Moriyama , Toshiki Abe , Hiroaki Saito , Isamu Amir , Chika Yamamoto , Mika Sato , Masaharu Tsubokura","doi":"10.1016/j.pdisas.2025.100446","DOIUrl":"10.1016/j.pdisas.2025.100446","url":null,"abstract":"<div><div>After the 2011 Great East Japan Earthquake, Soma City in Fukushima Prefecture, sustained extensive damage, and subsequently developed public housing for older victims, namely ‘Soma Idobata Nagaya’. The current interview survey aimed to report the living conditions of such older victims in Nagaya and accordingly suggest strategies for supporting the older population who experience disasters. We conducted semi-structured interviews with 32 Nagaya residents, who relocated there due to the earthquake, and performed a thematic analysis of the data. The development of Nagaya in Soma City after the earthquake enabled older residents to return to their familiar neighbourhoods and provided social security for those vulnerable to disasters. Beyond housing, Nagaya offered psychological stability and supported their independent living in a way they desired. It played a substantial role in rebuilding their lives, allowing them to overcome the hardships of the disaster and reintegrate into the local community. In summary, this study emphasizes the critical role of communities in post-disaster recovery and proposes new perspectives for supporting older adults in such contexts.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"27 ","pages":"Article 100446"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514136","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}
Pub Date : 2025-10-01Epub Date: 2025-07-29DOI: 10.1016/j.pdisas.2025.100454
Qiang Li, Zaohong Zhou, Yunbin Sun, Hongjun He
Fires in large public buildings cause substantial losses. Conducting reasonable evacuation risk assessments and simulation studies for early warning is essential. A comprehensive fire evacuation risk assessment model is proposed, consisting of a fire evacuation risk assessment framework based on Pythagorean fuzzy sets(PFS) and fuzzy comprehensive evaluation(FCE) and fire evacuation simulations. The fire evacuation risk assessment is conducted using PFS and FCE. Simulations with BIM, Pyrosim, and Pathfinder validate the results through dynamic safety analysis. This innovative approach enhances the dynamic safety analysis of evacuations. Additionally, the study improves the integration between Pyrosim and Pathfinder software, providing more accurate and reliable simulation results. Taking a cafeteria for primary and secondary school students as an example, the results indicate that the fire evacuation risk level of the student cafeteria is “moderately high risk.” Visibility is found to be the most critical factor affecting available safe evacuation time, compared to CO concentration, smoke layer height, and temperature. Not all cafeteria occupants could evacuate within the preset time, and significant congestion was observed. Thus, the assessment results are deemed reliable. Based on these results, targeted fire safety evacuation control strategies are proposed to enhance the efficiency and safety of evacuations in similar venues.
{"title":"Risk assessment and simulation optimization of evacuation in large public building fires: A case study","authors":"Qiang Li, Zaohong Zhou, Yunbin Sun, Hongjun He","doi":"10.1016/j.pdisas.2025.100454","DOIUrl":"10.1016/j.pdisas.2025.100454","url":null,"abstract":"<div><div>Fires in large public buildings cause substantial losses. Conducting reasonable evacuation risk assessments and simulation studies for early warning is essential. A comprehensive fire evacuation risk assessment model is proposed, consisting of a fire evacuation risk assessment framework based on Pythagorean fuzzy sets(PFS) and fuzzy comprehensive evaluation(FCE) and fire evacuation simulations. The fire evacuation risk assessment is conducted using PFS and FCE. Simulations with BIM, Pyrosim, and Pathfinder validate the results through dynamic safety analysis. This innovative approach enhances the dynamic safety analysis of evacuations. Additionally, the study improves the integration between Pyrosim and Pathfinder software, providing more accurate and reliable simulation results. Taking a cafeteria for primary and secondary school students as an example, the results indicate that the fire evacuation risk level of the student cafeteria is “moderately high risk.” Visibility is found to be the most critical factor affecting available safe evacuation time, compared to CO concentration, smoke layer height, and temperature. Not all cafeteria occupants could evacuate within the preset time, and significant congestion was observed. Thus, the assessment results are deemed reliable. Based on these results, targeted fire safety evacuation control strategies are proposed to enhance the efficiency and safety of evacuations in similar venues.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"27 ","pages":"Article 100454"},"PeriodicalIF":3.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771397","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}
Pub Date : 2025-10-01Epub Date: 2025-07-09DOI: 10.1016/j.pdisas.2025.100449
Md Touhidul Islam , Sujan Chandra Roy , Nusrat Jahan , Al-Mahmud , Md Mazharul Islam , Abdullah Al Ferdaus , Kazunori Fujisawa , A.K.M. Adham
Reliable forecasting of extreme river water levels is vital for managing flood and drought risks in Bangladesh, a deltaic nation highly vulnerable to climate change. This study compares nine machine learning (ML) models for predicting monthly maximum and minimum water levels at three key stations along the Old Brahmaputra River using a 34-year dataset (1990–2024). Performance was assessed using ten metrics including RMSE, R2, and NSE. Random Forest Regression (RFR) consistently outperformed other models, achieving the highest accuracy for both maximum (RMSE: 0.64–0.77 m; R2: 0.87–0.92) and minimum water levels (RMSE: 0.49–0.66 m; R2: 0.82–0.92), while linear models underperformed in capturing nonlinear patterns. A PCA-based framework further validated RFR's robustness, with average normalized composite scores of 1.00 (maximum) and 0.99 (minimum), significantly surpassing Ensemble Regression (0.89/0.84), Support Vector Regression (0.88/0.88), and other models. Spatially, midstream stations showed higher accuracy (R2 > 0.90) due to stable hydrodynamics, while downstream performance decreased from tidal effects. Key innovations including autoregressive lag features, sliding windows, and a multivariate evaluation framework significantly improved prediction accuracy. These findings demonstrate that ML models can enhance water level forecasting and disaster resilience in climate-vulnerable regions, even with limited data.
{"title":"Comparative evaluation of machine learning models for extreme river water level forecasting in Bangladesh: Implications for flood and drought resilience","authors":"Md Touhidul Islam , Sujan Chandra Roy , Nusrat Jahan , Al-Mahmud , Md Mazharul Islam , Abdullah Al Ferdaus , Kazunori Fujisawa , A.K.M. Adham","doi":"10.1016/j.pdisas.2025.100449","DOIUrl":"10.1016/j.pdisas.2025.100449","url":null,"abstract":"<div><div>Reliable forecasting of extreme river water levels is vital for managing flood and drought risks in Bangladesh, a deltaic nation highly vulnerable to climate change. This study compares nine machine learning (ML) models for predicting monthly maximum and minimum water levels at three key stations along the Old Brahmaputra River using a 34-year dataset (1990–2024). Performance was assessed using ten metrics including RMSE, R<sup>2</sup>, and NSE. Random Forest Regression (RFR) consistently outperformed other models, achieving the highest accuracy for both maximum (RMSE: 0.64–0.77 m; R<sup>2</sup>: 0.87–0.92) and minimum water levels (RMSE: 0.49–0.66 m; R<sup>2</sup>: 0.82–0.92), while linear models underperformed in capturing nonlinear patterns. A PCA-based framework further validated RFR's robustness, with average normalized composite scores of 1.00 (maximum) and 0.99 (minimum), significantly surpassing Ensemble Regression (0.89/0.84), Support Vector Regression (0.88/0.88), and other models. Spatially, midstream stations showed higher accuracy (R<sup>2</sup> > 0.90) due to stable hydrodynamics, while downstream performance decreased from tidal effects. Key innovations including autoregressive lag features, sliding windows, and a multivariate evaluation framework significantly improved prediction accuracy. These findings demonstrate that ML models can enhance water level forecasting and disaster resilience in climate-vulnerable regions, even with limited data.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"27 ","pages":"Article 100449"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604926","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}
Pub Date : 2025-10-01Epub Date: 2025-07-30DOI: 10.1016/j.pdisas.2025.100453
Mohammad Pourmatin , Elham Ajorlou , Ali Farhadzadeh , Majid Ghayoomi , Elizabeth Hewitt
Due to an increase in natural hazards, the cost of physical damage to local infrastructure has grown significantly. However, many vulnerabilities faced by the built environment involve human factors, which remain understudied. This study examines factors that influence how hydraulic and coastal engineers involved in U.S. flood infrastructure design perceive risk and integrate environmental and social considerations into their professional recommendations. A survey was conducted of U.S.-based civil engineers specializing in flood infrastructure design to assess factors influencing their design-related judgments. Using various statistical analyses, this study identifies key predictors shaping engineers' engagement with risk and climate. Results show that engineers with liberal political orientations are more likely to incorporate climate change impacts into designs, and household income is negatively associated with risk-aversion attitudes. Engineers with limited work experience report more influence from peers, and dissatisfaction with engineering education is a strong predictor of reliance on personal attitudes when facing uncertainty. While engineers are not the final decision-makers, these findings highlight their crucial role as intermediaries who shape how risk is framed and which options are presented to agencies and clients. These findings offer novel contributions merging social science with engineering and inform how decision-makers can enhance flood risk management.
{"title":"Human dimensions in flood risk management: Exploring risk perception and climate change considerations among engineers in the US","authors":"Mohammad Pourmatin , Elham Ajorlou , Ali Farhadzadeh , Majid Ghayoomi , Elizabeth Hewitt","doi":"10.1016/j.pdisas.2025.100453","DOIUrl":"10.1016/j.pdisas.2025.100453","url":null,"abstract":"<div><div>Due to an increase in natural hazards, the cost of physical damage to local infrastructure has grown significantly. However, many vulnerabilities faced by the built environment involve human factors, which remain understudied. This study examines factors that influence how hydraulic and coastal engineers involved in U.S. flood infrastructure design perceive risk and integrate environmental and social considerations into their professional recommendations. A survey was conducted of U.S.-based civil engineers specializing in flood infrastructure design to assess factors influencing their design-related judgments. Using various statistical analyses, this study identifies key predictors shaping engineers' engagement with risk and climate. Results show that engineers with liberal political orientations are more likely to incorporate climate change impacts into designs, and household income is negatively associated with risk-aversion attitudes. Engineers with limited work experience report more influence from peers, and dissatisfaction with engineering education is a strong predictor of reliance on personal attitudes when facing uncertainty. While engineers are not the final decision-makers, these findings highlight their crucial role as intermediaries who shape how risk is framed and which options are presented to agencies and clients. These findings offer novel contributions merging social science with engineering and inform how decision-makers can enhance flood risk management.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"27 ","pages":"Article 100453"},"PeriodicalIF":3.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757092","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}
Pub Date : 2025-10-01Epub Date: 2025-07-20DOI: 10.1016/j.pdisas.2025.100451
Suliman Zakaria Suliman Abdalla
The integration of Big Data Analytics (BDA) into Disaster Risk Management (DRM) presents transformative opportunities to enhance decision-making and foster environmental sustainability across preparedness, response, recovery, and resilience. This study investigates the factors influencing BDA adoption in DRM using an integrated Technology-Organization-Environment and Diffusion of Innovation (TOE-DOI) framework. Survey data collected from academic participants with backgrounds in statistics, data analysis, and quantitative methods, along with technical, management, and disaster response professionals, were analyzed using ordinal logistic regression to assess the impact of technological, organizational, and environmental predictors. Key findings show that technological enablers drive BDA adoption by enhancing prediction and efficiency, while organizational readiness supports sustained integration. Stakeholder collaboration promotes adoption through improved coordination. In contrast, regulatory and competitive factors were not significant. The study provides actionable insights for advancing DRM through multidisciplinary strategies that align BDA integration with sustainability goals, emphasizing its potential to support resilient systems and informed decision-making in the face of complex environmental challenges.
{"title":"Data-driven innovations in disaster risk management: Advancing resilience and sustainability through big data analytics","authors":"Suliman Zakaria Suliman Abdalla","doi":"10.1016/j.pdisas.2025.100451","DOIUrl":"10.1016/j.pdisas.2025.100451","url":null,"abstract":"<div><div>The integration of Big Data Analytics (BDA) into Disaster Risk Management (DRM) presents transformative opportunities to enhance decision-making and foster environmental sustainability across preparedness, response, recovery, and resilience. This study investigates the factors influencing BDA adoption in DRM using an integrated Technology-Organization-Environment and Diffusion of Innovation (TOE-DOI) framework. Survey data collected from academic participants with backgrounds in statistics, data analysis, and quantitative methods, along with technical, management, and disaster response professionals, were analyzed using ordinal logistic regression to assess the impact of technological, organizational, and environmental predictors. Key findings show that technological enablers drive BDA adoption by enhancing prediction and efficiency, while organizational readiness supports sustained integration. Stakeholder collaboration promotes adoption through improved coordination. In contrast, regulatory and competitive factors were not significant. The study provides actionable insights for advancing DRM through multidisciplinary strategies that align BDA integration with sustainability goals, emphasizing its potential to support resilient systems and informed decision-making in the face of complex environmental challenges.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"27 ","pages":"Article 100451"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686361","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}
Pub Date : 2025-10-01Epub Date: 2025-07-30DOI: 10.1016/j.pdisas.2025.100457
Shamima Prodhan, Khondoker Mokaddem Hossain, Md. Juel Mia
The COVID-19 has had a significant impact on various occupational groups in Bangladesh, disrupting their fundamental necessities and everyday activities. This study examines the governance responses to these occupational groups, focusing on transparency and accountability through the lens of the social contract theory. Unlike prior studies focused on economic impacts, this study reveals how governance failures exacerbated vulnerabilities of a critical gap in pandemic literature. By employing a mixed-methods approach, this study integrated qualitative techniques (key informant interviews and focus group discussions) with quantitative surveys to analyze both primary data collected from 355 respondents and secondary data from institutional reports and scholarly literature. A significant association was found between the pandemic's impact and occupational groups (X2(1, N = 355) =49.09, p = 0.000), highlighting job losses, reduced income, business closures, and salary deductions. A high prevalence of income dissatisfaction was observed, with 97.7 % of respondents expressing their discontent. Financial strain during different pandemic waves was evident, with t-values of 13.09 (first wave vs. pre-pandemic), −11.051 (first wave vs. second wave), and 8.073 (pre-pandemic vs. second wave), all p < 0.001. The government played a major role in providing food (p = 0.000) and health (p = 0.002) support; however, cash aid did not demonstrate statistical significance (p = 0.138). Gender inequalities were apparent in the provision of relief aid, with notable discrepancies in food assistance (p = 0.007), cash support (p < 0.001), and healthcare aid (p < 0.001). The study findings highlight the gaps in accountability and transparency in distributing support services, offering valuable insights for policymakers and researchers to enhance the resilience of impoverished populations in future crises. The study reveals novel insights into gendered aid disparities, urban-rural perception gaps, and systemic shortcomings in social safety nets during crises.
{"title":"Managing the post COVID-19 new normal: Redressing vulnerabilities of different occupational groups through social contract of public sector transparency and accountability","authors":"Shamima Prodhan, Khondoker Mokaddem Hossain, Md. Juel Mia","doi":"10.1016/j.pdisas.2025.100457","DOIUrl":"10.1016/j.pdisas.2025.100457","url":null,"abstract":"<div><div>The COVID-19 has had a significant impact on various occupational groups in Bangladesh, disrupting their fundamental necessities and everyday activities. This study examines the governance responses to these occupational groups, focusing on transparency and accountability through the lens of the social contract theory. Unlike prior studies focused on economic impacts, this study reveals how governance failures exacerbated vulnerabilities of a critical gap in pandemic literature. By employing a mixed-methods approach, this study integrated qualitative techniques (key informant interviews and focus group discussions) with quantitative surveys to analyze both primary data collected from 355 respondents and secondary data from institutional reports and scholarly literature. A significant association was found between the pandemic's impact and occupational groups (<em>X</em><sup>2</sup>(1, <em>N</em> = 355) =49.09, <em>p</em> = 0.000), highlighting job losses, reduced income, business closures, and salary deductions. A high prevalence of income dissatisfaction was observed, with 97.7 % of respondents expressing their discontent. Financial strain during different pandemic waves was evident, with t-values of 13.09 (first wave vs. pre-pandemic), −11.051 (first wave vs. second wave), and 8.073 (pre-pandemic vs. second wave), all <em>p</em> < 0.001. The government played a major role in providing food (<em>p</em> = 0.000) and health (<em>p</em> = 0.002) support; however, cash aid did not demonstrate statistical significance (<em>p</em> = 0.138). Gender inequalities were apparent in the provision of relief aid, with notable discrepancies in food assistance (<em>p</em> = 0.007), cash support (<em>p</em> < 0.001), and healthcare aid (p < 0.001). The study findings highlight the gaps in accountability and transparency in distributing support services, offering valuable insights for policymakers and researchers to enhance the resilience of impoverished populations in future crises. The study reveals novel insights into gendered aid disparities, urban-rural perception gaps, and systemic shortcomings in social safety nets during crises.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"27 ","pages":"Article 100457"},"PeriodicalIF":3.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757094","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}
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.
{"title":"Drought vulnerability assessment and mitigation strategies for peri-urban province of Pathum Thani, Thailand","authors":"Panita Saguansap , Prinya Mruksirisuk , Duangporn Garshasbi , 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}
Pub Date : 2025-04-02DOI: 10.1016/j.pdisas.2025.100420
Alberto de la Fuente , Carolina Meruane , Viviana Meruane
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.
{"title":"Ensemble weather-runoff forecasting models for reliable flood early warning systems","authors":"Alberto de la Fuente , Carolina Meruane , 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}
Pub Date : 2025-04-01Epub Date: 2025-02-17DOI: 10.1016/j.pdisas.2025.100410
Mujalin Intaramuean , Atsuko Nonomura , Tum Boonrod
The increasing frequency and severity of floods owing to climate change particularly affect children, making enhanced preparedness strategies essential for mitigating their impact. This study aimed to evaluate the effect of local topography maps on flood knowledge, flood risk perception (FRP), and flood preparedness (FP) among elementary school students aged 11–12 in Nakhon Si Thammarat Province, Thailand. A quasi-experimental design was employed, with 150 students divided into an experimental group (n = 75) and a control group (n = 75). The intervention comprised a flood education programme incorporating discussions, workshops, slide presentations, and interactive teaching methods to enhance students' knowledge, FRP, and FP. Data were collected via questionnaires at three-time points: pre-test (T0), post-test (T1), and follow-up (T2) between August and October 2023. Statistical analyses included t-tests, Wilcoxon rank-sum tests, ANOVA, and the Friedman test. The results indicated that the flood education programme significantly improved students' preparedness, though no significant differences in flood risk perception were observed between groups. The intervention highlighted the need to enhance students' understanding of local topography and flood hazard mapping. This study suggests integrating localized flood information into preparedness programs to improve knowledge, risk perception, and preparedness in classroom disaster education.
{"title":"Empowering flood preparedness: Enhancing flood knowledge, risk perception, and preparedness among primary school learners in flood-affected southern Thailand","authors":"Mujalin Intaramuean , Atsuko Nonomura , Tum Boonrod","doi":"10.1016/j.pdisas.2025.100410","DOIUrl":"10.1016/j.pdisas.2025.100410","url":null,"abstract":"<div><div>The increasing frequency and severity of floods owing to climate change particularly affect children, making enhanced preparedness strategies essential for mitigating their impact. This study aimed to evaluate the effect of local topography maps on flood knowledge, flood risk perception (FRP), and flood preparedness (FP) among elementary school students aged 11–12 in Nakhon Si Thammarat Province, Thailand. A quasi-experimental design was employed, with 150 students divided into an experimental group (n = 75) and a control group (n = 75). The intervention comprised a flood education programme incorporating discussions, workshops, slide presentations, and interactive teaching methods to enhance students' knowledge, FRP, and FP. Data were collected via questionnaires at three-time points: pre-test (T<sub>0</sub>), pos<em>t</em>-test (T<sub>1</sub>), and follow-up (T<sub>2</sub>) between August and October 2023. Statistical analyses included <em>t</em>-tests, Wilcoxon rank-sum tests, ANOVA, and the Friedman test. The results indicated that the flood education programme significantly improved students' preparedness, though no significant differences in flood risk perception were observed between groups. The intervention highlighted the need to enhance students' understanding of local topography and flood hazard mapping. This study suggests integrating localized flood information into preparedness programs to improve knowledge, risk perception, and preparedness in classroom disaster education.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"26 ","pages":"Article 100410"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463732","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}
<div><div>An in-depth understanding of diverse gender perspectives, pathways, and frameworks is pivotal for innovative and successful disaster response and resilience strategies across geographies. However, in most regions, gender perspectives in driving disaster resilience are either less operationalized, explored in research, or fragmented, creating unsustainable futures. The ramifications of these inequalities were foregrounded by the COVID-19 pandemic where the disproportionate vulnerability of individuals/genders became unavoidable. This reifies the need to create safety nets within disaster-resilient landscapes based on a gender-inclusive lens. In this study, 80 documents were systematically reviewed to explore the current and emerging gender perspectives (individual and institutional) towards disaster response and resilience mechanisms across geographies and over time. Findings highlight theoretical and conceptual deficits in the definition of gender and disaster response in the discourses. Additionally, disasters and disaster-induced impacts vary over time across genders and regions. They also reveal disproportionate disaster vulnerability among gender minorities and historically marginalized social groups. Furthermore, socioeconomic gender inequalities limit collective agency in disaster response while socio-cultural and patriarchal norms lead to uneven disaster response that are further reinforced by gender inequalities that lead to structural violence. Increased vulnerability to disasters increases fear and mistrust of existing institutional disaster management strategies. Response to Normative disaster management frameworks that entrench masculine dominance in disaster response through, emerging frameworks that draw from a critical feminist lens unfortunately feminize vulnerability and adversely limit gender-inclusive futures. It is acknowledged that place and social capital shape people's willingness to engage in disaster response across genders and regions. Therefore, collective social agency, social networks, and gender inclusion are catalytic towards the efficacy of disaster response and community resilience. Risk Communication for effective disaster response should leverage community institutions like schools, digital media platforms, and indigenous knowledge carriers to generate, mediate, and disseminate appropriate risk information. Five key strategies could drive gender-inclusive perspectives in disaster response and resilience, including (i) conducting context-based studies and research, (ii) use of novel research approaches, such as reflexive social learning, (iii) prioritizing incorporation of collective agency in policy and institutional frameworks, (iv) a research shift and focus towards uncovering the histories of vulnerability, and (v) development of transparent and feasible knowledge dissemination mechanisms. Increased participatory evidence-based research is needed, and policy frameworks must emphasize key pillars of
{"title":"Gender perspectives in disaster response: An evidence-based review","authors":"Alfred Acanga , Baker Matovu , Venugopalan Murale , Sudha Arlikatti","doi":"10.1016/j.pdisas.2025.100416","DOIUrl":"10.1016/j.pdisas.2025.100416","url":null,"abstract":"<div><div>An in-depth understanding of diverse gender perspectives, pathways, and frameworks is pivotal for innovative and successful disaster response and resilience strategies across geographies. However, in most regions, gender perspectives in driving disaster resilience are either less operationalized, explored in research, or fragmented, creating unsustainable futures. The ramifications of these inequalities were foregrounded by the COVID-19 pandemic where the disproportionate vulnerability of individuals/genders became unavoidable. This reifies the need to create safety nets within disaster-resilient landscapes based on a gender-inclusive lens. In this study, 80 documents were systematically reviewed to explore the current and emerging gender perspectives (individual and institutional) towards disaster response and resilience mechanisms across geographies and over time. Findings highlight theoretical and conceptual deficits in the definition of gender and disaster response in the discourses. Additionally, disasters and disaster-induced impacts vary over time across genders and regions. They also reveal disproportionate disaster vulnerability among gender minorities and historically marginalized social groups. Furthermore, socioeconomic gender inequalities limit collective agency in disaster response while socio-cultural and patriarchal norms lead to uneven disaster response that are further reinforced by gender inequalities that lead to structural violence. Increased vulnerability to disasters increases fear and mistrust of existing institutional disaster management strategies. Response to Normative disaster management frameworks that entrench masculine dominance in disaster response through, emerging frameworks that draw from a critical feminist lens unfortunately feminize vulnerability and adversely limit gender-inclusive futures. It is acknowledged that place and social capital shape people's willingness to engage in disaster response across genders and regions. Therefore, collective social agency, social networks, and gender inclusion are catalytic towards the efficacy of disaster response and community resilience. Risk Communication for effective disaster response should leverage community institutions like schools, digital media platforms, and indigenous knowledge carriers to generate, mediate, and disseminate appropriate risk information. Five key strategies could drive gender-inclusive perspectives in disaster response and resilience, including (i) conducting context-based studies and research, (ii) use of novel research approaches, such as reflexive social learning, (iii) prioritizing incorporation of collective agency in policy and institutional frameworks, (iv) a research shift and focus towards uncovering the histories of vulnerability, and (v) development of transparent and feasible knowledge dissemination mechanisms. Increased participatory evidence-based research is needed, and policy frameworks must emphasize key pillars of ","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"26 ","pages":"Article 100416"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578766","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号