Progress in Disaster Science最新文献

英文中文

Flood mapping, damage assessment, and susceptibility zonation in northeastern Bangladesh in 2022 using geospatial datasets

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-01-01 DOI: 10.1016/j.pdisas.2024.100402

Md. Mizanur Rahman , Mohammad Kamruzzaman , Limon Deb , H.M. Touhidul Islam

This study assesses flood inundation, impacts, and susceptibility zones in northeastern Bangladesh during the 2022 flood. The region is highly vulnerable to recurrent flooding Due to its geographic position and climate change impacts. The Sentinel-1 SAR data on the Google Earth Engine (GEE) platform was used to generate flooded areas using a simple change detection technique with thresholding. This analysis was further supported by incorporating cropland, population, national highway, and DEM datasets for a comprehensive damage assessment. Findings show that 55.76 % (10,993.09 km²) of the area was inundated, impacting 10.69 million people and causing severe displacement and health hazards. Sylhet, Kishoreganj, and Brahmanbaria districts were the most affected, with 2.73 million impacted in Sylhet alone. Additionally, 67.87 % of agricultural land was flooded, particularly in Sunamganj, and 43.38 % of national highways (535.08 km²) were damaged. A flood susceptibility zonation map identified high-susceptibility areas like central Sunamganj and parts of Kishoreganj to assist authorities in resource allocation and mitigation. The flood extent model achieved strong predictive accuracy (AUC: 0.97 % RF, 0.96 % LR, and 0.94 % DT), providing crucial insights for regional flood management and guiding communities with limited modeling capacities.

{"title":"Flood mapping, damage assessment, and susceptibility zonation in northeastern Bangladesh in 2022 using geospatial datasets","authors":"Md. Mizanur Rahman , Mohammad Kamruzzaman , Limon Deb , H.M. Touhidul Islam","doi":"10.1016/j.pdisas.2024.100402","DOIUrl":"10.1016/j.pdisas.2024.100402","url":null,"abstract":"<div><div>This study assesses flood inundation, impacts, and susceptibility zones in northeastern Bangladesh during the 2022 flood. The region is highly vulnerable to recurrent flooding Due to its geographic position and climate change impacts. The Sentinel-1 SAR data on the Google Earth Engine (GEE) platform was used to generate flooded areas using a simple change detection technique with thresholding. This analysis was further supported by incorporating cropland, population, national highway, and DEM datasets for a comprehensive damage assessment. Findings show that 55.76 % (10,993.09 km<sup>2</sup>) of the area was inundated, impacting 10.69 million people and causing severe displacement and health hazards. Sylhet, Kishoreganj, and Brahmanbaria districts were the most affected, with 2.73 million impacted in Sylhet alone. Additionally, 67.87 % of agricultural land was flooded, particularly in Sunamganj, and 43.38 % of national highways (535.08 km<sup>2</sup>) were damaged. A flood susceptibility zonation map identified high-susceptibility areas like central Sunamganj and parts of Kishoreganj to assist authorities in resource allocation and mitigation. The flood extent model achieved strong predictive accuracy (AUC: 0.97 % RF, 0.96 % LR, and 0.94 % DT), providing crucial insights for regional flood management and guiding communities with limited modeling capacities.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"25 ","pages":"Article 100402"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153116","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}

引用次数: 0

Women's knowledge and perception of flood disasters in Butaleja District, Uganda

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-01-01 DOI: 10.1016/j.pdisas.2024.100399

Alice Nakiyemba, Kinobe Zakaria, Kakungulu Mosses, Nyangoma Immeldah, Masaba Sowedi

Globally, flood disasters have increased, adversely affecting women. The study assessed women's knowledge and perception of flood disasters in Butaleja district, Eastern Uganda, regarding the occurrence, severity, causes, and timely access to flood information among women flood victims. Evaluating women's knowledge and perception of flood disasters guides the design and implementation of risk-reduction initiatives and practices. We employed mixed methods, with primary data collected from 300 households, 16 focus group discussions, and 9 key informants. Data were analysed with R-software and Atlas ti 23. Results attributed the causes of flood disasters to heavy rainfall, poor farming methods, and encroachment on wetlands and riverbanks. Most women reported that floods were becoming more severe, and they lacked access to information sources regarding flood disasters. Women's awareness of flood disasters is significantly associated with the respondents' level of education and the primary source of livelihood. The study concludes that even when women flood victims were aware of flood disaster occurrence, severity, and causes, they exhibited inadequate knowledge, as they did not have access to information sources to alert them to flood disasters. The study recommends appropriate location of flood early warning systems and proper land use to enhance women's knowledge of flood disasters.

{"title":"Women's knowledge and perception of flood disasters in Butaleja District, Uganda","authors":"Alice Nakiyemba, Kinobe Zakaria, Kakungulu Mosses, Nyangoma Immeldah, Masaba Sowedi","doi":"10.1016/j.pdisas.2024.100399","DOIUrl":"10.1016/j.pdisas.2024.100399","url":null,"abstract":"<div><div>Globally, flood disasters have increased, adversely affecting women. The study assessed women's knowledge and perception of flood disasters in Butaleja district, Eastern Uganda, regarding the occurrence, severity, causes, and timely access to flood information among women flood victims. Evaluating women's knowledge and perception of flood disasters guides the design and implementation of risk-reduction initiatives and practices. We employed mixed methods, with primary data collected from 300 households, 16 focus group discussions, and 9 key informants. Data were analysed with R-software and Atlas ti 23. Results attributed the causes of flood disasters to heavy rainfall, poor farming methods, and encroachment on wetlands and riverbanks. Most women reported that floods were becoming more severe, and they lacked access to information sources regarding flood disasters. Women's awareness of flood disasters is significantly associated with the respondents' level of education and the primary source of livelihood. The study concludes that even when women flood victims were aware of flood disaster occurrence, severity, and causes, they exhibited inadequate knowledge, as they did not have access to information sources to alert them to flood disasters. The study recommends appropriate location of flood early warning systems and proper land use to enhance women's knowledge of flood disasters.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"25 ","pages":"Article 100399"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153111","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}

引用次数: 0

Machine learning for human mobility during disasters: A systematic literature review

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-01-01 DOI: 10.1016/j.pdisas.2025.100405

Jonas Gunkel , Max Mühlhäuser , Andrea Tundis

Understanding and predicting human mobility during disasters is crucial for effective disaster management. Knowledge about population locations can greatly enhance rescue missions and evacuations. Realistic models that reflect observable mobility patterns and volumes are crucial for estimating population locations. However, existing models are limited in their applicability to disasters, as they are typically restricted to describing regular mobility patterns. Machine learning models trained to capture patterns observable in provided training data also face this limitation. The necessity of large amounts of training data for machine learning models, coupled with the scarcity of data on mobility in disasters, often constrains the feasibility of their training. Various strategies have been developed to overcome this issue, which we present and discuss in this systematic literature review. Our review aims to support and accelerate the synthesis of novel approaches by establishing a knowledge base for future research. This review identified a condensed field of related contributions exhibiting high methodology and context diversity. We classified and analyzed the relevant contributions based on their proposed approach and subsequently discussed and compared them qualitatively. Finally, we elaborated on general challenges and highlighted areas for future research.

{"title":"Machine learning for human mobility during disasters: A systematic literature review","authors":"Jonas Gunkel , Max Mühlhäuser , Andrea Tundis","doi":"10.1016/j.pdisas.2025.100405","DOIUrl":"10.1016/j.pdisas.2025.100405","url":null,"abstract":"<div><div>Understanding and predicting human mobility during disasters is crucial for effective disaster management. Knowledge about population locations can greatly enhance rescue missions and evacuations. Realistic models that reflect observable mobility patterns and volumes are crucial for estimating population locations. However, existing models are limited in their applicability to disasters, as they are typically restricted to describing regular mobility patterns. Machine learning models trained to capture patterns observable in provided training data also face this limitation. The necessity of large amounts of training data for machine learning models, coupled with the scarcity of data on mobility in disasters, often constrains the feasibility of their training. Various strategies have been developed to overcome this issue, which we present and discuss in this systematic literature review. Our review aims to support and accelerate the synthesis of novel approaches by establishing a knowledge base for future research. This review identified a condensed field of related contributions exhibiting high methodology and context diversity. We classified and analyzed the relevant contributions based on their proposed approach and subsequently discussed and compared them qualitatively. Finally, we elaborated on general challenges and highlighted areas for future research.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"25 ","pages":"Article 100405"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153142","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}

引用次数: 0

Multiple hazards and population change in Japan’s Suzu City after the 2024 Noto Peninsula Earthquake

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-01-01 DOI: 10.1016/j.pdisas.2024.100396

Shohei Nagata , Erick Mas , Yuriko Takeda , Tomoki Nakaya , Shunichi Koshimura

The earthquake that struck Japan’s Noto Peninsula on January 1, 2024, caused extensive damage, leading to the first major tsunami warning since the 2011 Tohoku earthquake. It remains unclear where people moved immediately after the earthquake and how earthquake-induced multiple hazards affected human mobility, reflecting evacuation movement. This study examines the human mobility change in Suzu City, severely damaged by strong ground shaking and multiple hazards, including tsunamis and liquefaction, from January 1 to January 3, 2024, using population estimates based on cell phone networks. Specifically, we unravel the detailed spatiotemporal changes in population distribution in the affected areas, reflecting evacuation from the tsunami and other multiple hazard factors. Our results reveal that immediately after the earthquake, people concentrated in inland areas, suggesting that the major tsunami warning facilitated the evacuation from the coast to inland areas. Furthermore, the sense of strong ground shaking and tsunami inundation risk may have triggered tsunami evacuation. A clear drop in population was delayed by one to two days after the earthquake in areas with a high liquefaction potential and landslide occurrence. This study’s outcomes contribute to a better understanding of human mobility during disasters, thereby aiding future disaster-management decisions.

{"title":"Multiple hazards and population change in Japan’s Suzu City after the 2024 Noto Peninsula Earthquake","authors":"Shohei Nagata , Erick Mas , Yuriko Takeda , Tomoki Nakaya , Shunichi Koshimura","doi":"10.1016/j.pdisas.2024.100396","DOIUrl":"10.1016/j.pdisas.2024.100396","url":null,"abstract":"<div><div>The earthquake that struck Japan’s Noto Peninsula on January 1, 2024, caused extensive damage, leading to the first major tsunami warning since the 2011 Tohoku earthquake. It remains unclear where people moved immediately after the earthquake and how earthquake-induced multiple hazards affected human mobility, reflecting evacuation movement. This study examines the human mobility change in Suzu City, severely damaged by strong ground shaking and multiple hazards, including tsunamis and liquefaction, from January 1 to January 3, 2024, using population estimates based on cell phone networks. Specifically, we unravel the detailed spatiotemporal changes in population distribution in the affected areas, reflecting evacuation from the tsunami and other multiple hazard factors. Our results reveal that immediately after the earthquake, people concentrated in inland areas, suggesting that the major tsunami warning facilitated the evacuation from the coast to inland areas. Furthermore, the sense of strong ground shaking and tsunami inundation risk may have triggered tsunami evacuation. A clear drop in population was delayed by one to two days after the earthquake in areas with a high liquefaction potential and landslide occurrence. This study’s outcomes contribute to a better understanding of human mobility during disasters, thereby aiding future disaster-management decisions.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"25 ","pages":"Article 100396"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153109","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}

引用次数: 0

Capturing information needs in disaster situations by using temporal and spatial offset learning (TSOL)

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-01-01 DOI: 10.1016/j.pdisas.2024.100392

Kota Tsubouchi, Shuji Yamaguchi

This paper presents a framework for identifying the offline information needs of persons in disaster situations by analyzing online behavioral logs and utilizing the users' location and search history. Two main challenges are addressed: accurately identifying persons most affected by the situation from noisy location data and distinguishing event-related search queries from unrelated ones. To tackle these challenges, we propose a machine-learning method, called temporal and spatial offset learning (TSOL), that incorporates both temporal and spatial distinctiveness. TSOL assigns heavier weights to these dimensions, in order to offset complexities and uncertainties surrounding user information. We validated the effectiveness of TSOL through experiments in actual disaster situations. The proposed framework and TSOL offer a promising approach to capturing and analyzing the information needs of individuals affected by disasters. The captured information needs in disaster situations have often been reported on TV in Japan as a support of those affected by disasters.

引用次数: 0

Real-time decision support model for logistics of emergency patient transfers from hospitals via an integrated optimisation and machine learning approach

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-01-01 DOI: 10.1016/j.pdisas.2024.100397

Maziar Yazdani, Siroos Shahriari, Milad Haghani

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.

{"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, Siroos Shahriari, 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}

引用次数: 0

Southern Iranian households preparedness in disasters and relationship with demographic factors

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-01-01 DOI: 10.1016/j.pdisas.2024.100401

Aman Allah Zamani , Abdoljabbar Zakeri , Shokrollah Mohseni , Gholamali Javdan , Ali Azarpeikan , Fatemeh Azadi , Hassan Morshedi , Vajihe Shamsaei , Akram Ahmadizadeh Fini

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.

{"title":"Southern Iranian households preparedness in disasters and relationship with demographic factors","authors":"Aman Allah Zamani , Abdoljabbar Zakeri , Shokrollah Mohseni , Gholamali Javdan , Ali Azarpeikan , Fatemeh Azadi , Hassan Morshedi , Vajihe Shamsaei , 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}

引用次数: 0

Lessons from the 2024 Noto Peninsula Earthquake: Need for digital transformation in disaster response

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-01-01 DOI: 10.1016/j.pdisas.2024.100400

Sakiko Kanbara , Rajib Shaw , Kiyotaka Eguchi , Sangita Das

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.

{"title":"Lessons from the 2024 Noto Peninsula Earthquake: Need for digital transformation in disaster response","authors":"Sakiko Kanbara , Rajib Shaw , Kiyotaka Eguchi , 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}

引用次数: 0

Machine learning approaches for seismic vulnerability assessment of urban buildings: A comparative study with analytic hierarchy process

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-01-01 DOI: 10.1016/j.pdisas.2024.100398

Danesh Asadollahzadeh, Behrouz Behnam

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 %.

{"title":"Machine learning approaches for seismic vulnerability assessment of urban buildings: A comparative study with analytic hierarchy process","authors":"Danesh Asadollahzadeh, 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}

引用次数: 0

Low-income households' willingness to pay for flood risk insurance in South Africa

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-01-01 DOI: 10.1016/j.pdisas.2024.100403

David Lefutso , Abiodun A. Ogundeji , Gideon Danso-Abbeam , Yong S. Nyam

As climate change leads to increase flood risks, South Africa continues to rely on government driven ex post 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.

{"title":"Low-income households' willingness to pay for flood risk insurance in South Africa","authors":"David Lefutso , Abiodun A. Ogundeji , Gideon Danso-Abbeam , 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}

引用次数: 0

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Progress in Disaster Science

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀