Disaster Damage Research Articles

Recent Studies on Alternative Fuel of Dimethyl Ether

With the development of industrialization, a large number of non-renewable fuels (such as coal and crude oil) are consumed, and the harmful substances produced in the combustion process of a large number of fossil fuels have caused serious pollution to the atmosphere, and the harmful gases produced by combustion have caused disastrous damage to the ecological balance. Therefore, finding clean energy and exploring alternative fuels are very important in today's society. This paper mainly reviews the studies on the alternative fuels of dimethyl ether (DME). Firstly, the types of alternative fuels currently researched by society and their respective advantages and disadvantages are analyzed, and the preparation of dimethyl ether and its advantages and disadvantages are analyzed in detail. In addition, the physicochemical properties, combustion and emission characteristics of dimethyl ether and diesel are compared and analyzed. The conclusion is that the injection delay angle of dimethyl ether is larger than that of diesel, the ignition delay period is shorter than that of diesel, and the maximum explosion pressure, maximum pressure rise rate and combustion noise of dimethyl ether are lower than that of diesel. The diffusion combustion speed of DME is faster than that of diesel, and the combustion duration is shorter than that of diesel. At the same time, as an alternative energy, dimethyl ether engine has a significant reduction in NOx emission, a very low level of HC and CO emission, and zero soot emission. In conclusion, the DME engine has good performance and emission characteristics.

Anthropogenic gravitational mass movements and the fluvial geomorphological changes: The Vale (2019) and Samarco (2015) tailing dam disasters, Brazil

Major tailings dam failures have caused death, pain, and disruption to a community’s activities. These dam ruptures mobilize millions of tonnes of tailings, which move with gravity and can therefore be called anthropogenic mass movements. A survey of the typology of the damage caused is a necessity to assess the damage caused, especially to riverside populations. This work describes the mass movements in the 2015 Samarco/Mariana and 2019 Vale/Brumadinho tailing dam disasters. Mass movements have been described as avalanches, high and low-energy debris flows, and mud flows. The regions where the tailings dispersion was active were also verified, where it caused substantial changes in the landscape. On the other hand, it has been verified in areas where this dispersion was passive, carried out only within the fluvial dynamics. The Vale/Brumadinho disaster, which resulted in 270 deaths, was of tremendous energy and destructive power, while the Samarco/Mariana disaster, which resulted in 19 deaths, was catastrophic over thousands of kilometers. A careful evaluation of these modifications is a first step toward mitigating the affected territories and bringing social justice to the affected populations.

Vale tudo: As estratégias de atuação da Vale após o desastre-crime da barragem de Córrego do Feijão

Abstract Purpose: This article aims to analyze the strategies used by Vale during the first two years after the Brumadinho dam disaster to deal with criticisms from civil society in the face of the consequences caused by the rupture. Originality/value: The article contributes to understanding the strategic processes used by large enterprises to respond to environmental disasters and other damages resulting from activities and work accidents caused by these organizations. It demonstrates how such strategies can be applied and succeed because they are linked to actions and articulations with key actors involved and co-opted in economic, political, and social processes before the disaster. Design/methodology/approach: A qualitative, case study type of research was conducted with citizens of Brumadinho and local mobilization groups. The interviews conducted with ten citizens of the municipality and document analysis allowed a volume of information from the various actors that make up and participate in the tangle surrounding the actions related to this disaster. Findings: The company uses mechanisms that impute discouragement and disbelief in the affected people, fomenting the politics of resignation through numerous strategies of bureaucratization, social and mental suffering, and rights violations that postpone the reparation actions to the affected people. The article demonstrates how the context of technological disasters involves a political, economic, institutional, and social process marked by violations, influences, and injustices with communities and people whose beginning is before the disasters.

A Framework to Assess Remote Sensing Algorithms for Satellite-Based Flood Index Insurance

Remotely sensed data has the potential to monitor natural hazards and their consequences on socio-economic systems. However, in much of the world, inadequate validation data of disaster damage make reliable use of satellite data difficult. We attempt to strengthen the use of satellite data for one application - flood index insurance - which has the potential to manage the largely uninsured losses from floods. Flood index insurance is a particularly challenging application of remote sensing due to floods' speed, unpredictability, and the significant data validation required. We propose a set of criteria for assessing remote sensing flood index insurance algorithm performance and provide a framework for remote sensing application validation in data-poor environments. Within these criteria, we assess several validation metrics – spatial accuracy compared to high-resolution PlanetScope imagery (F1), temporal consistency as compared to river water levels (Spearman's ρ), and correlation to government damage data (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) – that measure index performance. With these criteria we develop a Sentinel-1 flood inundation time series in Bangladesh at high spatial (10m) and temporal (∼weekly) resolution and compare it to a previous Sentinel-1 algorithm and a MODIS time series used in flood index insurance. Results show that the adapted Sentinel-1 algorithm (F1 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">avg</sub> =0.925, ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">avg</sub> =0.752, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.43) significantly outperforms previous Sentinel-1 and MODIS algorithms on the validation criteria. Beyond Bangladesh, our proposed validation criteria can be used to develop and validate better remote sensing products for index insurance and other flood applications in places with inadequate ground truth damage data.

Temporal and Spatial Distribution Characteristics of Flood Disasters with Different Intensities in Arid-Semiarid Region in Northern Xinjiang, China

Floods have devastating environmental and socioeconomic consequences. Flood disaster management is critical for the long-term management of water resources and agriculture in arid areas. Using the data on the number of deaths, destroyed homes, collapsed sheds, livestock deaths, and the crops affected area caused` by 1394 regional floods in Northern Xinjiang (As a typical arid-semiarid region of NorthWest China) from 1981 to 2019, the ratio-weight method and the dimensionless linear summation method was used to calculate the actual disaster damage exponent of flood disaster events. The damage exponent is further used to categorize the severity of disaster events into four grades: normal, moderate, severe, and extremely severe. The analysis results indicated that Bortala Mongolian Autonomous Prefecture experienced the highest frequency of catastrophic occurrences, while Ili River Valley experienced the highest intensity. The flood happened most often in Wenquan County. And Yining County suffered the worst calamity. The occurrence frequency and intensity of disasters from Grades 1 to 3 exhibited a unimodal distribution, with the majority and most vital occurrences in July, whereas the occurrence frequency and intensity of Grade 4 disasters followed a bimodal distribution, with the most occurrences in July and the strongest in April. The annual frequency of heavy rain and flood disasters in Northern Xinjiang increased by 7.7 times every 10 years, indicating a significant linear increase trend. The yearly occurrences of Grades 1 and 2 also increased linearly, by 5.2 and 2.0 times per decade, respectively. There was no linear tendency to increase or decrease flood disasters in Grades 3 and 4. The analysis and division of actual disaster damage are conducive to flood risk management, efficient prevention, and reducing disaster losses.

One-class Damage Detector Using Deeper Fully Convolutional Data Descriptions for Civil Application

Infrastructure managers must maintain high standards to ensure user satisfaction during the lifecycle of infrastructures. Surveillance cameras and visual inspections have enabled progress in automating the detection of anomalous features and assessing the occurrence of deterioration. However, collecting damage data is typically time consuming and requires repeated inspections. The one-class damage detection approach has an advantage in that normal images can be used to optimize model parameters. Additionally, visual evaluation of heatmaps enables us to understand localized anomalous features. The authors highlight damage vision applications utilized in the robust property and localized damage explainability. First, we propose a civil-purpose application for automating one-class damage detection reproducing a Fully Convolutional Data Description (FCDD) as a baseline model. We have obtained accurate and explainable results demonstrating experimental studies on concrete damage and steel corrosion in civil engineering. Additionally, to develop a more robust application, we applied our method to another outdoor domain that contains complex and noisy backgrounds using natural disaster datasets collected using various devices. Furthermore, we propose a valuable solution of deeper FCDDs focusing on other powerful backbones to improve the performance of damage detection and implement ablation studies on disaster datasets. The key results indicate that the deeper FCDDs outperformed the baseline FCDD on datasets representing natural disaster damage caused by hurricanes, typhoons, earthquakes, and fourevent disasters.

Comparative Study of Real-Time Semantic Segmentation Networks in Aerial Images During Flooding Events

Real-time semantic segmentation of aerial imagery is essential for unmanned ariel vehicle applications, including military surveillance, land characterization, and disaster damage assessments. Recent real-time semantic segmentation neural networks promise low computation and inference time, appropriate for resource-limited platforms, such as edge devices. However, these methods are mainly trained on human-centric view datasets, such as Cityscapes and CamVid, unsuitable for aerial applications. Furthermore, we do not know the feasibility of these models under adversarial settings, such as flooding events. To solve these problems, we train the most recent real-time semantic segmentation architectures on the FloodNet dataset containing annotated aerial images captured after hurricane Harvey. This article comprehensively studies several lightweight architectures, including encoder–decoder and two-pathway architectures, evaluating their performance on aerial imagery datasets. Moreover, we benchmark the efficiency and accuracy of different models on the FloodNet dataset to examine the practicability of these models during emergency response for aerial image segmentation. Some lightweight models attain more than 60% test mIoU on the FloodNet dataset and yield qualitative results on images. This article highlights the strengths and weaknesses of current segmentation models for aerial imagery, requiring low computation and inference time. Our experiment has direct applications during catastrophic events, such as flooding events.

재난피해자의 회복수준에 대한 지역공동체 회복탄력성의 조절효과

This study examines the moderating effect of community resilience on the relationship between disaster damage types and recovery level of disaster victims. It was conducted with the aim of seeking ways to enhance community resilience from the perspective of community welfare. It means, intended to examine the mitigation effect of community resilience on the negative relationship between disaster damage experience and recovery level. Community resilience is one of the important concepts in disaster management. It is one of the social resources needed when community experiences a crisis such as a disaster. This study was using the data from ‘2021 Disaster Victims Damage and Recovery research on the actual condition' of National Disaster Management Institute(NDMI). The data came from a sample 1,807 respondents(household) who experienced disasters in south korea, from 2018 to 2020. The program ’SPSS WIN 25.0’ was used for descriptive analysis, Pearson's correlation and regression analysis. The research results are as follows. First, it was found that economic, psychological, physical, and daily difficulties from the disaster experiences had a negative (-) significant effect on the level of recovery. Second, it was found that community resilience had a significant moderating effect on the negative relationship between economic and psychological damage and the level of recovery among disaster damage experiences. This study is meaningful in that it empirically confirmed that it is necessary to enhance the resilience of the local community in order to increase the level of recovery of disaster victims. In order to enhance the resilience of the community, social welfare practices of the local community were proposed.

Google Trend Özelinde Kullanıcıların Afetlere Yönelik İlgi Düzeylerinin Belirlenmesi

Afetler, toplumların kendi kaynakları ile baş edemeyecekleri durumlardır. Dolayısıyla afetlere hazırlıkla toplum kapasitesinin artırılması germektedir. Toplumların kırılganlık düzeyleri tespit edilerek afet hazırlık planlarına dâhil edilmesi toplumsal kapasitenin artırılması açısından önemlidir. Toplumların afet eğitimlerine ve alanına yönelik ilgi düzeylerinin afetlere hazırlanma noktasında önemli bir paydaya sahip olduğu düşünülmektedir. Gerçekleştirilen bu çalışmada da; afetle ilgili seçilmiş bazı anahtar kelimeler üzerinden toplumun ilgili düzeyi belirlenmeye çalışılmıştır. Çalışmada; genel olarak kullanıcıların ilgi düzeyini yaygın olarak belirleyebilecek “Deprem”, “Deprem Duası”, “Deprem Toplanma Alanı”, “Çök-Kapan-Tutun”, “Deprem Anında Ne Yapılmalı”, “Deprem Çantasında Neler Olmalı” gibi anahtar kelimelerle sorgulama yapılmıştır. İlgili kelimelerin yoğunlukta trend olduğu dönemlerin depremlerin yaşandığı dönemlere gelmesi dikkat çekici bir nokta olarak belirlenmiştir. Ayrıca eğitim faaliyetlerinin düzenlediği haftalarda da bazı kelimelerin trend olduğu saptanmıştır. Sonuç olarak; toplumların genel olarak olay esnasında ve sonrasında ilgili konulara daha duyarlıklı oldukları söylenebilir. Ancak afet zararlarının en aza indirgenebilmesi için afet öncesi hazırlık çalışmalarına daha fazla ilgi gösterilmesi gerektiği açıktır. Bu bağlamda afet öncesi hazırlık faaliyetlerinin topyekûn gerçekleştirilmesinin dirençliliği artıracağı ifade edilebilir.

Study on typhoon disaster assessment by mining data from social media based on artificial neural network

Typhoon disaster is a major threat to the economy and personnel safety in coastal areas. After the disaster, the objective assessment of typhoon disaster economic losses can provide an important reference for the post-disaster rescue and reconstruction and improve the scientific decision making. In this study, social media data, disaster causing factors, disaster bearing carriers (exposure and vulnerability) and other factors in traditional disasters were combined to achieve rapid disaster assessment. Convolutional neural networks were used to train a text classifier and perform automatic text classification of social media data. The correlations were discussed between various texts and disaster economic losses. It was found that there was a strong correlation between the geographical distribution of texts describing disaster damage and disaster economic losses. A back-propagation neural network was used for supervised learning to realize disaster loss assessment. To prove the reliability and applicability of the evaluation model, typhoons “Mangkhut” and “Lekima” were selected as study cases to realize the whole process from information collection to final assessment. The introduction of social media data modified the assessment results obtained by traditional methods and reduced the difference between the estimated disaster loss value and the actual value.

The role of female volunteers in disaster response organisations: A qualitative research.

Despite a high demand for people to participate in disaster volunteering, the extent of contribution received from female volunteers in disaster response organisations constitutes an important debate. In the Elazığ earthquake on 24 January 2020, the role of female volunteers in rescuing a female victim from the wreckage once again demonstrated the importance of considering female volunteers when creating response capacities for disaster risks. This qualitative study aims to evaluate the roles of female volunteers who play an active role in disaster response organisations in a society with gender equality issues. This qualitative study included 15 National Medical Rescue Team (UMKE) female volunteers who took an active role in the Elazig earthquake and was performed between 15.07.2020 and 16.04.2021. Data were collected using an in-depth interview method. The data were analyzed using content analysis and the comments were given descriptively. Based on the qualitative analysis, the following concepts were explored among the participants: Volunteering, volunteering in disasters, the effects of volunteering on social life, being a woman in disasters, volunteering female in disasters, the 'motherhood myth', gender inequality. Women are key actors in tackling disaster damage and increasing available social resilience to disasters. In addition, women can use special attributes such as motherhood, which is socio-culturally defined and taught as an advantage, for the common good of society. Moreover, special elements, such as the image of the 'superwoman' in relation to the responsibilities imposed on women in disaster response organisations, which distort gender equality against women, appear as possible factors of harm for women. This study shows that female volunteers can play a life-saving role in chaotic situations such as disasters, contribute to the empowerment of women, increase sensitivity to gender and, in connection with this, the development of democracy in societies where gender inequality is high. On the other hand, special situations such as the image of 'superwoman', which aims for excellence in both private and public spheres in the responsibilities imposed on women, can destroy gender equality against women and increase women's fragility.

재난관리를 위한 주정보 지원에 관한 연구

This study focuses on strengthening citizens’ capacity as disaster management agents and on facilitating the provision of housing information by finding ways to prepare for disasters by analyzing the case of housing information centers in Japan. The results are as follows. 1) The victims of the disaster asked for counseling and information about restoration of house and life. The housing information centers installed in Osaka, Kobe, and Kyoto as housing information support platforms were able to respond quickly. 2) The housing information centers confirmed that it was necessary to expand the counseling system available in case of disaster as a forum for counseling, education, and networking. In addition, it was necessary to educate on the importance of self-help, form networks between citizens, businesses, and organizations, and establish a company selection system. 3) Housing information centers provide a counseling and information service for disaster prevention education and disaster damage prevention. In an emergency, the housing information center serves as a window for counseling and information provision regarding the housing stability and reconstruction of victims.

Debris flow susceptibility based on the connectivity of potential material sources in the Dadu River Basin

The Dadu River Basin (DRB) is an important economic corridor on the Chuanxi Plateau. The DRB is located in the highly changeable terrain of the eastern Qinghai-Tibet Plateau and is often threatened by debris flows. To mitigate the damage of debris flow disasters to the construction and operation of large infrastructures such as railways, roads and hydropower stations in the DRB, it is necessary to evaluate debris flow susceptibility. The connectivity of potential material sources (CPMS), which proxies the possibility of potential material transfer from the source to the outlet, was calculated using an improved index of connectivity, excess topography and brittle fracture index to identify and quantify debris flow susceptibility. The results show that the IC and the gully scale have a Logartihmic function in the DRB, in which IC decreases as the catchment area increases. In 3967 gullies of the Dadu River and its tributaries, the total connectivity of potential material sources (TCPMS) could be used to distinguish between debris flow gullies and non-debris flow gullies (AUC = 0.748). The TCPMS and average connectivity of potential material sources (ACPMS) were used to classify the scale and activity intensity of the 2398 debris flow gullies into four levels. The moderate- and high-activity intensity and large- and extra large-scale debris flows are mainly distributed in Danba, Luding and Kangding. The results of this study provide reference and evidence for the identification and evaluation of debris flow susceptibility.

THE ROLE OF NEWSPAPER IN DISSEMINATING INFORMATION IN AMPHAN AFFECTED AREAS: A STUDY ON PIROJPUR DISTRICT

In general, Bangladesh's coastal region has been hit by a lot of disasters, but the frequency of these disasters has grown in recent years. Providing adequate information can help lessen the damage caused by these events. Newspapers are vital medium for disseminating information to the public about impending disasters specially in the coastal areas of Bangladesh. This study is all about analyzing the role of newspapers in disseminating information about the Cyclone Amphan to common people's consciousness. Qualitative and quantitative approaches were used in this study. The information was gathered through content analysis of four national dailies and interviews with two groups: victims of the Amphan affected area in the Pirojpur district and journalists from national dailies working in the same district. The study finds that there was insufficient coverage of Cyclone Amphan concerns in The Daily Star (5%), The Daily Prothom Alo (9%), The Daily Ittefaq (7%), and Bangladesh Pratidin (6%) of the total news. From the analysis, newspapers carried disaster-related news to warn the public in disaster-prone areas about the disaster's damage and measures, as well as to alert affected individuals about the disaster's position and signal. Despite the low coverage of disaster news, newspapers attracted people's attention because they provided accurate and credible news and allowed them to learn more about a diverse range of topics.

The effects of information publicity and government subsidy on port climate change adaptation: Strategy and social welfare analysis

This paper develops an integrated economic model to examine two competing ports’ investment in adaptation to climate-change disasters. The ports have asymmetric information on the actual disaster damage. In deciding on adaptation investment, the “leader” port is a better-informed first mover and the “follower” port is a less-informed follower. The government is able to acquire and verify port adaptation information and chooses whether to publicize it (publicity policy) or keep it confidential (confidentiality policy). If the information is publicized, the leader port initiates a signaling game in which it makes a decision first, and the follower acts after observing the leader's decision. Under the confidentiality policy, both ports make their decisions simultaneously. Our analysis shows the following. 1) Under the publicity policy, the leader port does not adopt a pooling strategy; rather, it determines its adaptation and output based on true information on disaster damage (i.e., it implements a separating equilibrium). 2) The publicity policy has two effects: it increases the leader's throughput while decreasing the follower's throughput (i.e., the throughput effect), and it helps rationalize the follower's adaptation investment by making more up-to-date disaster information available (i.e., the adaptation investment effect). 3) When the degree of inter-port competition is high, the publicity policy improves social welfare by increasing the total throughput of the two ports (i.e., an overall positive throughput effect) and rationalizes the follower's adaptation investment (i.e., a positive adaptation investment effect). Otherwise, it reduces the total throughput of the ports (i.e., an overall negative throughput effect). This negative throughput effect may outweigh the positive adaptation investment effect, thereby reducing social welfare. These analytical results suggest that the government needs to promote inter-port competition if it decides to publicize port adaptation information. Finally, we find that the joint implementation of publicity and subsidy policies could result in excessive adaptation investment, leading to social welfare loss. Our study illustrates the important effects of information asymmetry on adaptation investment and has policy implications in the presence of port competition and government subsidy.

CrowdNAS: A Crowd-guided Neural Architecture Searching Approach to Disaster Damage Assessment

Disaster damage assessment (DDA) has emerged as an important application in disaster response and management, which aims to assess the damage severity of an affected area by leveraging AI (e.g., deep learning) techniques to examine the imagery data posted on social media during a disaster event. In this paper, we focus on a crowd-guided neural architecture searching (NAS) problem in DDA applications. Our goal is to leverage human intelligence from crowdsourcing systems to guide the discovery of the optimal neural network architecture in the design space to achieve the desirable damage assessment performance. Our work is motivated by the limitation that the deep neural network architectures in current DDA solutions are mainly designed by AI experts, which is known to be both time-consuming and error-prone. Two critical technical challenges exist in solving our problem: i) it is challenging to design a manageable NAS space for crowd-based solutions; ii) it is non-trivial to transfer the imperfect crowd knowledge to effective decisions in identifying the optimal neural network architecture of a DDA application. To address the above challenges, we develop CrowdNAS, a crowd-guided NAS framework that develops novel techniques inspired by AI, crowdsourcing, and estimation theory to address the NAS problem. The evaluation results from two real-world DDA applications show that CrowdNAS consistently outperforms the state-of-the-art AI-only, crowd-AI, and NAS baselines by achieving the highest classification accuracy in the damage assessment while maintaining a low computational cost under various evaluation scenarios.

How stock market reacts to environmental disasters and judicial decisions: A case study of Mariana’s dam collapse in Brazil

This study examines the stock market reaction to one of the major environmental disasters of the world mining industry: the Mariana dam collapse in Brazil. Based on an event study, we evaluated the impact on the mining companies’ abnormal returns surrounding the disaster and also investigated whether post-event judicial decisions affected the companies. Our results show a significant negative effect around the days of the event, reporting a 5 % drop in daily returns. Regarding the legal efforts, our findings suggest the coordination time and the benefits granted by authorities as being interpreted positively, reducing market’s expectation of an agile or severe punishment following Mariana’s dam disaster.

Factors Associated with Housing Damage Caused by an EF4 Tornado in Rural Areas of Funing, China.

Rural areas are vulnerable to natural disasters and tend to suffer severe losses. An EF4 tornado occurred in Funing on 23 June 2016, killing 99 people, injuring at least 846 people, and destroying more than 2000 houses. Using a multinomial logistic regression model, this study explored the influencing factors between housing damage and variables of building conditions, tornado intensity, and village environmental factors. The results show that 2-story houses and masonry houses were more likely to be slightly damaged or be in a dangerous state. Furthermore, the building area was positively related to houses in two categories: slight damage (SD) and dangerous and requiring immediate repair (DR), indicating that the larger or taller the house, the more severe the damage. In terms of tornado intensity, houses classified as SD were more likely to be hit by EF4 tornados than by EF3 tornados, and houses were damaged more by EF1 or EF2 tornados. This finding demonstrates that the level of housing damage was not strongly correlated with the tornado intensity. Slightly damaged houses exhibited the highest correlation with environmental factors. The proportion of slightly damaged houses was positively correlated with the water area in the village, unlike the proportion of houses in the DR and unable to be repaired (UR) categories. Moreover, the larger the water area of a village, the less housing damage it suffered. These findings provide new insights into minimizing housing damage in wind disasters to improve disaster prevention planning in rural areas.

Evaluation of safe-to-fail flood solution alternatives and integration of safe-to-fail concept in AEC education to develop resilient coastal cities

Purpose Safe-to-fail (SF) is an emerging resilient design approach that has the potential to minimize the severity of flood damages. The purpose of this study is to explore the SF design strategies to reduce flood disaster damages in US coastal cities. Therefore, this study addresses two research questions: identifying the most suitable SF criteria and flood solution alternatives for coastal cities from industry professionals’ perspective; and investigating the controlling factors that influence the AEC students’ interest to learn about SF concepts through the curricula. Design/methodology/approach This study used the analytical hierarchy process to evaluate the SF criteria and flood solutions where data were collected through surveying 29 Department of Transportation professionals from different states. In addition, the study adopted a quantitative methodology by surveying 55 versed participants who reside in a coastal area and have coastal flood experiences. The data analysis included ordinal probit regression and descriptive analysis. Findings The results suggest that robustness is the highest weighted criterion for implementing SF design in coastal cities. The results demonstrated that ecosystem restoration is the highest-ranked SF flood solution followed by green infrastructure. Moreover, the results highlighted that age, duration spent in the program and prior knowledge of SF are significantly related to AEC students’ interest to learn this concept. Originality/value SF design anticipates failures while designing infrastructures thus minimizing failure consequences due to flood disasters. The findings can facilitate the implementation of the SF design concept during the construction of new infrastructures in coastal cities as well as educate the future workforces to contribute to developing resilient built environments.

City Models and Preventive Planning Strategies for Resilient Cities in Germany

In the face of the Covid-19 crisis, the city model of the new Leipzig Charter of the EU was re-evaluated. The existing urban development model of a mixed and compact city is to be mainly maintained because the urban density or building typology does not influence the spread of Covid-19. But the pandemic has made it clear how important green space and recreation areas are for inner city residential areas. This green space also becomes more important regarding climate adaptation measures to provide cooler air and ventilation. In the framework of the Leipzig Charter of the EU, the German ministry for building adopted the memorandum on Urban Resilience in May 2021. Resilience in this context means that we should not only repair the damage of disasters but also adapt to future crises and make our cities more resilient and sustainable. For this, we need to strengthen preventive strategies in urban development planning connected with urban renewal approaches and ask for extended city models. Planning shapes the future, including counteracting undesirable scenarios with preventive planning. In this sense, future planning and disaster control have common objectives—they take an interdisciplinary approach to prepare for future change, they want to anticipate and prevent danger, protect and expand the infrastructure, and serve the common good. In this article, I will point out how integrated urban development concepts should be extended with aspects of urban resilience, and which city models are important for the future.