International journal of disaster risk reduction最新文献

{"title":"Holistic mapping of flood vulnerability in slums areas of Yaounde city, Cameroon through household and institutional surveys","authors":"Desmond N. Shiwomeh ,&nbsp;Sameh A. Kantoush ,&nbsp;Tetsuya Sumi ,&nbsp;Binh Quang Nguyen ,&nbsp;Karim I. Abdrabo","doi":"10.1016/j.ijdrr.2024.104947","DOIUrl":"10.1016/j.ijdrr.2024.104947","url":null,"abstract":"<div><div>Urbanization in major cities has resulted in increasing urban slum expansion. This, together with increased climate-change-driven hazards, and deplorable slum characteristics has led to considerably higher flood impacts in slum settlements. As such, there is a need for specialized flood vulnerability assessment tools that integrate features specific to the urban slums. Studies have consecrated efforts to integrated and multidimensional flood vulnerability studies. However, assessments that include social, economic, structural, and institutional realities of the slum settlements are rare in developing countries. This study comprehensively assessed the flood vulnerability in urban slums. It offers a simplified perspective of vulnerability in urban slums, capturing data from slum inhabitants, local councils, experts, and local NGOs since they often have profound insights into essential service availability, access, and quality within the study area. Utilizing data encompassing 40 indicators (exposure, susceptibility, and resilience), we assess the physical/structural, social, and economic/psychological vulnerability indices for slum households and the institutional vulnerability of 41 entities. Despite significant challenges of poor infrastructure and lack of basic disaster management tools, slum residents have developed recognizable strategies to overcome flooding. Institutions carrying out intervention activities in the slums were largely incompetent and plagued with challenges ranging from lack of technical know-how to access to funds and coordination. Finally, a significant gap exists between state efforts and the impacts of these efforts on the residents of these slums. These findings complement household-level data and provide an expanded understanding of vulnerability patterns, thus informing policymakers about interventions.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"114 ","pages":"Article 104947"},"PeriodicalIF":4.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142660985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing urban fire risk: An ensemble learning approach based on scenarios and cases","authors":"Shibo Cui,&nbsp;Ning Wang,&nbsp;Enhui Zhao,&nbsp;Jing Zhang,&nbsp;Chunli Zhang","doi":"10.1016/j.ijdrr.2024.104941","DOIUrl":"10.1016/j.ijdrr.2024.104941","url":null,"abstract":"<div><div>Urban fires represent a significant hazard to people’s lives and property, which makes it critical to estimate the risk adequately. Existing urban fire evaluation methods lack applicability because they do not take into account individual scene components and previous cases. As a result, this study offers the scenario- and case-based urban fire risk assessment approach (SCBUFRA), which seeks to achieve a more thorough and accurate urban fire risk assessment. First, the technique uses fire case and scenario data, as well as the recursive feature elimination method, to pick the elements utilized to assess urban fire risk. Second, the data-driven empowerment technique and stability analysis are utilized to determine the precise fire risk value and correctly quantify the fire danger level in each part of the city. Next, the Affinity Propagation (AP) technique is used to cluster scene elements. Ensemble learning is then used to create a risk prediction model by refining the weighting strategy of <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>. Finally, Shapley additive explanations are used to investigate the elements causing urban fires. The findings show that SCBUFRA outperforms popular machine learning methods, that the number of crimes, gross population, and house price are the most important variables for fire prediction, and that the research is applicable to urban fire risk management and firefighting resource allocation.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"114 ","pages":"Article 104941"},"PeriodicalIF":4.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142660995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying uncertainty in landslide susceptibility mapping due to sampling randomness","authors":"Lei-Lei Liu ,&nbsp;Shuang-Lin Zhao ,&nbsp;Can Yang ,&nbsp;Wengang Zhang","doi":"10.1016/j.ijdrr.2024.104966","DOIUrl":"10.1016/j.ijdrr.2024.104966","url":null,"abstract":"<div><div>The quality of landslide and non-landslide samples plays a crucial role in landslide susceptibility maps (LSMs) generated using machine learning algorithms. However, uncertainties arising from the collection of non-landslide samples can significantly compromise the reliability of these maps. Current methods, such as buffer-controlled sampling (BCS), often fail to address this issue adequately. This study aims to fill that gap by employing Monte Carlo simulations combined with BCS to quantify the uncertainties associated with non-landslide sampling and improve the accuracy of LSMs. A novel framework is proposed by incorporating landslide susceptibility confidence maps (LSCMs) to address the inherent uncertainty in BCS-based LSMs. The framework evaluates inconsistencies in LSMs, showing that maps generated by the same model may differ in over 30 % of the area due to variations in selection of non-landslide samples. The proposed approach outperforms traditional methods by correctly classifying landslide-prone areas, particularly in low and very low susceptibility zones, while providing a more reliable quantification of uncertainty. These findings underscore the limitations of traditional LSM methods and demonstrate that LSCMs offer a more robust tool for landslide hazard assessment. The framework enhances the precision of susceptibility mapping and provides critical insights for better risk mitigation and disaster preparedness.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"114 ","pages":"Article 104966"},"PeriodicalIF":4.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel spatial-aware deep learning approach for exploring the environmental context of terrorist attacks and armed conflicts","authors":"Zhan'ao Zhao ,&nbsp;Kai Liu ,&nbsp;Ming Wang","doi":"10.1016/j.ijdrr.2024.104921","DOIUrl":"10.1016/j.ijdrr.2024.104921","url":null,"abstract":"<div><div>The quantitative assessment of terrorist attacks and armed conflicts (TAACs) is a crucial component of global public safety research and is vital for societal stability and national security. This study addresses the spatial dependency of such events, i.e., the relationship between the outbreak of an event and its environment. Based on geographic big data and artificial intelligence (AI), we propose a spatial feature utilization pattern that takes into account the impact of the event environment, and established a deep learning (DL) framework of features within the joint event location and space neighborhood to improve the precision of the quantitative assessment. The results demonstrate that in scenarios under a combination of 14 social, natural, and geographic driving factors, models that incorporate spatial features outperform those that only use location features during both the training and testing phases. Furthermore, models that consider both location and spatial features outperform models using only a single feature across various evaluation metrics. Global attribution analysis further confirms the spatial dependency of events, manifested in the mutual influence on the likelihood of events occurring among adjacent cities and the correlation with various environmental factors, particularly elements related to human activities and living environments. We find that both prosperous urban centers and underdeveloped rural areas are hotspots for TAACs, and that such events more likely to occur in harsh climatic patterns characterized by high temperatures and low precipitation. This enhances our understanding and preparedness for managing and preventing such events.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"114 ","pages":"Article 104921"},"PeriodicalIF":4.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Population activity recovery: Milestones unfolding, temporal interdependencies, and relationship with physical and social vulnerability","authors":"Flavia-Ioana Patrascu ,&nbsp;Ali Mostafavi","doi":"10.1016/j.ijdrr.2024.104931","DOIUrl":"10.1016/j.ijdrr.2024.104931","url":null,"abstract":"<div><div>Understanding sequential community recovery milestones is crucial for proactive recovery planning and monitoring and targeted interventions. This study investigates these milestones related to population activities to examine their temporal interdependencies and evaluate the relationship between recovery milestones and physical (residential property damage) and socioeconomic vulnerability (through household income). This study leverages post-2017 Hurricane Harvey mobility data from Harris County to specify and analyze temporal recovery milestones and their interdependencies. The analysis examined four key milestones: return to evacuated areas, recovery of essential and non-essential services, and the rate of home-switch (moving out of residences). Robust linear regression validates interdependencies between across milestone lags and sequences: achieving earlier milestones accelerates subsequent recovery milestones. The study thus identifies six primary recovery milestone sequences. We found that socioeconomic vulnerability accounted through the median household income level, rather than physical vulnerability to flooding accounted through the property damage extent, correlates with recovery delays between milestones. We studied variations in recovery sequences across lower and upper quantiles of property damage extent and median household income: lower property damage extent and lower household income show greater representation in the “slowest to recover” sequence, while households with greater damage and higher income are predominant in the group with the “fastest recovery sequences”. Milestone sequence variability aligns closely with income, independent of physical vulnerability. This empowers emergency managers to effectively monitor and manage recovery efforts, enabling timely interventions.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"114 ","pages":"Article 104931"},"PeriodicalIF":4.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial analysis of major industrial risks of petroleum origin in urban areas - The case of the city of Hassi-Messaoud","authors":"Lahcene Bouzouaid ,&nbsp;Kamal Youcef","doi":"10.1016/j.ijdrr.2024.104916","DOIUrl":"10.1016/j.ijdrr.2024.104916","url":null,"abstract":"<div><div>Aiming at an efficient management of its oil sector and ensuring safety for its population and property, Algeria is currently engaged in all-out assessment approach. Efficient management and safety prove to be crucial parameters in oil-related activity. The major risks degree of the severity of whatever nature have impacts of various and diverse dimensions. The current study presents an occasional paradox case that which combines all high-risk elements and specific factors associated with them in an urban environment, which is made fragile and vulnerable due to its heavy exposure to a highly probable danger. The city of Hassi-Messaoud, the most important component of the Country's economy, witnesses an alarming spatial development driven by an exceptional population growth. The latter is primarily expressed through the incessant influx of immigrants attracted by promising job prospects in the oil industry sector. Coupled with the uncontrolled population movement, the urban expansion lends itself to the most dramatic aspect of Hassi-Messaoud and eventually exposes it to certain dangers all the more as Hassi-Messaoud is located in an area subject to significant potential oil-based risks.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"114 ","pages":"Article 104916"},"PeriodicalIF":4.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying post-disaster community well-being: A case study of Hurricane Harvey","authors":"Mohamadali Morshedi ,&nbsp;Makarand Hastak ,&nbsp;Satish V. Ukkusuri ,&nbsp;Seungyoon Lee","doi":"10.1016/j.ijdrr.2024.104903","DOIUrl":"10.1016/j.ijdrr.2024.104903","url":null,"abstract":"<div><div>Natural hazards such as hurricanes affect various aspects of the community members’ lives and their post-disaster well-being by causing significant disruptions in the key community activities in the immediate recovery phase. Furthermore, natural hazards leave behind short-term socio-economic impacts such as stress, anxiety, huge recovery expense, and lack of affordable housing. There is a need for incorporating both immediate and short-term impacts of natural hazards when measuring disaster recovery. This study aims to address this need by introducing community well-being as the metric for the recovery of communities from natural disasters. From this perspective, community resilience is defined as the capability of community to reach its pre-disaster state of well-being, in a timely and efficient manner. The study leverages Bottom-Up Spillover Theory and the existing literature to introduce a community well-being model. This model quantifies how the functionality of infrastructure systems can affect various aspects of community well-being based on 6 domains, 17 sub-domains, and 51 indicators. The indicators were quantified using survey data and 211-call data for the City of Houston, and data on the impact of Hurricane Harvey at the zip code level. The results showed that various dimensions of well-being were affected heterogeneously and followed different recovery patterns. The proposed framework can serve decision makers as a dashboard for identifying the well-being domains and sub-domains that should be addressed to enhance post-disaster well-being in the immediate-to short-term. Furthermore, the study introduces the phone call data as an inexpensive and timely replacement for multiple rounds of survey questionnaires for quantifying community well-being.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"114 ","pages":"Article 104903"},"PeriodicalIF":4.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disaster awareness levels and institutional responsibility perceptions of international students in Turkey","authors":"Salih Ciftci,&nbsp;Leyla Ciftci","doi":"10.1016/j.ijdrr.2024.104939","DOIUrl":"10.1016/j.ijdrr.2024.104939","url":null,"abstract":"<div><div>This study aimed to investigate the disaster awareness levels and institutional responsibility perceptions of international students in Turkey. Turkey is a country that is prone to natural disasters, and it is important to receive disaster training to be prepared for disasters. Turkey hosts more than three hundred thousand international students from 198 different countries. Bartın is one of the cities where international students receive education. Bartın is a risky city in terms of disasters such as earthquakes, floods, and landslides. This is why it is highly important for international students living in Bartın to receive disaster training. Other important issues include which institutions they would reach in a disaster situation, how they would reach them, and how they should act during a disaster. It was determined that 40 % of the participants had not received disaster training and were not sufficiently knowledgeable about relevant institutions. It was also observed that some of the responses of the participants were influenced by their gender, age, duration of living in Turkey, whether there was a risk of disasters in their home country, disaster experiences, whether they experienced loss in disasters, and whether they had received disaster-related training.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"114 ","pages":"Article 104939"},"PeriodicalIF":4.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How have regional evacuation conditions changed over time? Evacuation model for alternative scenarios given the accident environment, regional environment, and social systems","authors":"Weihua Zhang ,&nbsp;Wenmei Gai ,&nbsp;Wuyi Cheng ,&nbsp;Liaoying Zhou","doi":"10.1016/j.ijdrr.2024.104959","DOIUrl":"10.1016/j.ijdrr.2024.104959","url":null,"abstract":"<div><div>The rapid evacuation in major leak accidents during hazardous chemical transportation is critical for protecting people in risk areas. An approach integrating the accident environment, regional environment, and social system is proposed to perform evacuation evaluation in such accidents. An agent-based modeling framework consisting of warning mechanisms, evacuation preparation process, as well as evacuation modes and movement process, is developed to comprehensively model the evacuation process. The regional environmental and social system data from 1964 to 2020 of Jiangsu Province in China is applied to analyze whether the changes in evacuation conditions have correspondingly affected evacuation effectiveness. A regional evacuation model simulating a real liquid chlorine leak accident during transportation in Wuzhong of Jiangsu Province is constructed to test the effectiveness and applicability of the proposed method. It is found that a 9 % change in the diffusion rate of warning messages triggered by environmental cues with the evolution of the study area settlements following the chronological changes; while the evolution of media technology allows for the rapid diffusion of warning messages and the rapid loading of a large number of individuals into the regional evacuation network; Years with faster warning diffusion have not been the most efficient ones in terms of overall evacuation; The distribution of buildings and population substantially impacts the overall evacuation effectiveness, while the former has a more significant impact. The study results can provide information for public emergency authorities to develop effective early warning resource allocation and evacuation organization plans.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"114 ","pages":"Article 104959"},"PeriodicalIF":4.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FloodDamageCast: Building flood damage nowcasting with machine-learning and data augmentation","authors":"Chia-Fu Liu ,&nbsp;Lipai Huang ,&nbsp;Kai Yin ,&nbsp;Sam Brody ,&nbsp;Ali Mostafavi","doi":"10.1016/j.ijdrr.2024.104971","DOIUrl":"10.1016/j.ijdrr.2024.104971","url":null,"abstract":"<div><div>Near-real-time estimation of damages (a.k.a, damage nowcasting) to building and infrastructure is crucial during response and recovery efforts. Despite advancements in flood risk predictions, the majority of existing methods primarily focus on inundation estimation with limited damage nowcasting capabilities. Flooding damage nowcasting at fine spatial resolutions remains a very challenging problem with currently no existing model to perform the task. This limitation is mainly due to a number of technical challenges such as limited consideration of non-linear interactions between flood hazards and build-environment features, issues with imbalanced datasets, and the absence of reliable ground truth for model performance evaluation. To address this important gap, this study presents FloodDamageCast, a machine learning (ML) framework tailored for property flood damage nowcasting. The framework leverages heterogeneous data related to the built environment, topographic, and hydrological features to predict residential flood damage in a fine resolution of 500 m by 500 m in the context of Harris County, TX, during the 2017 Hurricane Harvey. To deal with data imbalance, FloodDamageCast includes a tabular data augmentation model based on Conditional Tabular Generative Adversarial Networks (CTGAN). The data augmentation model component addresses highly imbalanced class issues, where the majority class constitutes 96.4% of the dataset, potentially impairing model performance, By combining GAN-based data augmentation with an efficient ML model, Light Gradient-Boosting Machine (LightGBM), our results demonstrate the framework’s ability to identify high-damage spatial areas that would be overlooked by baseline models. the satisfactory performance of FloodDamageCast also shows its capability to be used for flood damage nowcasting at a fine spatial resolution to inform response and recovery efforts. The insights from flood damage nowcasting would help emergency management agencies and public officials to more efficiently identify repair needs and allocate resources, and also save time and efforts during on-the-ground inspections.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"114 ","pages":"Article 104971"},"PeriodicalIF":4.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}