Showing papers in "International journal of disaster risk reduction in 2021"

COVID-19 risk perception and coping mechanisms: Does gender make a difference?

Abstract: The novel coronavirus disease (COVID-19) emerged as a real threat to humans, drastically disrupting everyday life in 2020-21. Although the pandemic affected people from all walks of life, irrespective of age or gender, the way the risk is perceived varies from one person to another. The pandemic risk reduction strategies can only be effective if individuals and communities respond positively to them, and for that, it is important to understand how the risk is perceived and responded to, differently by different groups of people. Gender plays a vital role in shaping risk perceptions and coping strategies, reflecting the predisposition of the public to accept health interventions and take precautionary measures. This study aims to understand the gender differences in COVID-19 risk perception and coping mechanisms - Pakistan is selected as a case study area. Following on from designing the questionnaire, which included 40 indicators grouped into domains (four risk perception and three coping mechanisms domains), an online survey was conducted, and a sample of 389 respondents was collected (248 male and 141 female). An index-based approach was used to quantify risk perception and its domains (fear, behaviour, awareness, and trust), and likewise coping mechanisms and their domains (problem, emotion, and action). Statistical tests were employed to ascertain the differences among both genders, whereas regression modelling was used to measure the effect of gender on overall risk perception and coping mechanisms. Results reveal that perceived fear and trust varied significantly between Pakistani men and women, while coping mechanisms were also notably different between the two genders. Females were found to perceive risks higher, complied more with the government's guidelines, and coped better than males in response to COVID-19. These findings imply that the gender aspect must be incorporated in designing effective communication and risk reduction strategies to efficiently address the current and potential future pandemic situations.

Evaluating urban flood risk using hybrid method of TOPSIS and machine learning

Abstract: With the growth of cities, urban flooding has increasingly become an issue for regional and national governments. The destructive effects of floods are magnified in cities. Accurate models of urban flood susceptibility are required to mitigate this hazard mitigation and build resilience in cities. In this paper, we evaluate flood riskin Jiroft city, Iran, using a combination of machine learning and decision-making methods. Flood hazard maps were created using three state-of-the-art machine learning methods (support vector machine, random forest, and boosted regression tree). The metadata supporting our analysis comprises 218 flood inundation points and a variety of derived factors: slope aspect, elevation, slope angle, rainfall, distance to streets, distance to rivers, land use/land cover, distance to urban drainages, urban drainage density, and curve number. We then employed the TOPSIS decision-making tool for urban flood vulnerability analysis, which is based on socio-economic factors such as building density, population density, building history, and socio-economic conditions. Finally, we derived an urban flood risk map for Jiroft based on flood hazard and vulnerability maps. Of the three models tested, the random forest model yielded the most accurate map. The results indicate that urban drainage density and distance to urban drainages are the most important factors in urban flood hazard modeling. As might be expected, areas with a high or very high population density are most vulnerable to flooding. These results show that flood risk mapping provide insights for priority planning in flood risk management, especially in areas with limited hydrological data.

Innovations in earthquake risk reduction for resilience: recent advances and challenges

Abstract: The Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR) highlights the importance of scientific research, supporting the ‘availability and application of science and technology to decision making’ in disaster risk reduction (DRR). Science and technology can play a crucial role in the world’s ability to reduce casualties, physical damage, and interruption to critical infrastructure due to natural hazards and their complex interactions. The SFDRR encourages better access to technological innovations combined with increased DRR investments in developing cost-effective approaches and tackling global challenges. To this aim, it is essential to link multi- and interdisciplinary research and technological innovations with policy and engineering/DRR practice. To share knowledge and promote discussion on recent advances, challenges, and future directions on ‘Innovations in Earthquake Risk Reduction for Resilience’, a group of experts from academia and industry met in London, UK, in July 2019. The workshop focused on both cutting-edge ‘soft’ (e.g., novel modelling methods/frameworks, early warning systems, disaster financing and parametric insurance) and ‘hard’ (e.g., novel structural systems/devices for new structures and retrofitting of existing structures, sensors) risk-reduction strategies for the enhancement of structural and infrastructural earthquake safety and resilience. The workshop highlighted emerging trends and lessons from recent earthquake events and pinpointed critical issues for future research and policy interventions. This paper summarises some of the key aspects identified and discussed during the workshop to inform other researchers worldwide and extend the conversation to a broader audience, with the ultimate aim of driving change in how seismic risk is quantified and mitigated.

Perceived impact of the Covid-19 crisis on SMEs in different industry sectors: Evidence from Sichuan, China

Abstract: The outbreak of the novel coronavirus (Covid-19) has led to a global public health disaster and global economic crisis and significantly impacted industries across the world. As China was the first to feel the effects of the Covid-19 outbreak, it was also the first to overcome the effects and resume economic production. To understand the impact of the Covid-19 pandemic on SMEs in China and suggest public policies to deal with the negative effects, in February 2020, this paper examined the impacts on 3194 SMEs from primary, manufacturing, wholesale and retail trade, hospitality (accommodation and catering), and new economy industries in Sichuan, China using an online survey and follow-up interviews. It was found that the effects differed by industry sector: the primary industry sector by poor logistics; the manufacturing industry sector by supply chain management problems; the wholesale and retail trade industry sector by the need to accelerate their online services; the hospitality industry sector, the most severely affected sector, by cash flow pressure; and the new economy industry sector by short-term pressures. Short-term revenue declines and an inability to resume work and production were common problems faced by all surveyed SMEs. These findings from Sichuan, China provide some valuable references for global industry recovery.

Stakeholder perceptions in flood risk assessment: A hybrid fuzzy AHP-TOPSIS approach for Istanbul, Turkey

Abstract: As an initial exploration, preliminary studies which are conducted for the identification of key points to be addressed in flood risk management (FRM) processes are crucial to mitigate potential impacts of floods. Generating the flood risk maps with the participation of diverse stakeholders at each level of administration is essential to develop effective FRM strategies. Hence, the objectives of this study are twofold: i) to produce district-based vulnerability, hazard, and flood risk maps for Istanbul with a hybrid fuzzy AHP-TOPSIS model, ii) to generate these maps by considering the perceptions of different stakeholders separately, which is an initial attempt in the literature, ensuring the comparative analysis of stakeholder perceptions in FRM. Local and metropolitan municipalities, disaster management and coordination centres, water and sewerage administrations, and universities were considered as the leading stakeholders since they are chiefly responsible decision-making bodies in FRM practices in Istanbul. Pearson's correlation coefficient and Spearman's rank correlation coefficient tests were used to obtain a more accurate understanding of the agreement levels between stakeholders. The results revealed the need for the involvement of various stakeholders to generate flood risk maps since significantly different perspectives were observed; and the need for changing the generated flood risk maps. The findings of this study are critical because generated maps show distinct differences according to the mentality of the organizations and experts, which inevitably change the most flood-prone areas and possible mitigation investments.

Factors affecting intention to prepare for mitigation of “the big one” earthquake in the Philippines: Integrating protection motivation theory and extended theory of planned behavior

Abstract: The lack of studies on Filipinos preparedness in natural calamities such as earthquakes, specifically “The Big One”, posed the necessity for researchers to assess the preparedness and disaster mitigation. This study aimed to assess the driving factors that significantly influence the intention to prepare among Filipinos for mitigation of The Big One earthquake. With the integration of Protection Motivation Theory and Extended Theory of Planned Behavior, the study considered 727 valid responses to measure the intention to prepare. Eight latent were measured namely: (1) perceived vulnerability, (2) perceived severity, (3) subjective norm, (4) perceived behavioral control, (5) attitude, (6) media, (7) understanding of The Big One, and (8) intention to prepare. By utilizing Structural Equation Modelling, it is found out that Media, Attitude, Perceived Severity and Subjective Norm are all key factors affecting the intention of the people to prepare for the Big One. Moreover, it was also found out that the Understanding of the Big One has an indirect effect on the intention to prepare. The findings of the study can be utilized by the government to make Filipinos for conducting preparedness and mitigation practices. Finally, the model construct of the study could also be utilized to evaluate other types of natural disasters worldwide.

Damage classification of residential buildings in historical downtown after the ML5.5 earthquake in Zagreb, Croatia in 2020

Abstract: A severe earthquake hit Zagreb on the March 22, 2020 (magnitude ML = 5.5, with an epicenter 7 km north of the city center). During the COVID-19 lockdown, the event occurred and caused significant damage to the built environment and enormous disruption in everyday life. Brief facts about the Zagreb earthquake, the typology of buildings in the city, and the data collection after the quake itself are described. The paper focuses on post-earthquake assessment, damage classification, and failure patterns of residential buildings in Zagreb's old historical town, where masonry structures prevail. The earthquake critically damaged buildings that are important architectural achievements and ruptured Zagreb's historically recognizable city center. Furthermore, the data collected in the rapid post-earthquake assessment were analyzed and discussed. Graphical representations of damages are detailed and accompanied by photographs. This earthquake exposed Croatian building stock's vulnerability that should be mitigated as efficiently as possible in the coming years.

Medical students as the volunteer workforce during the COVID-19 pandemic: Polish experience

Abstract: In response to the COVID-19 pandemic medical students in different countries were mobilized to support healthcare systems during the emergency This study presents the experience of 580 students of a single medical university in Poland who served as volunteers at different healthcare units during the first six months of the first case being recorded in the country (March–September 2020) The mean ± SD hours and days spent on volunteering in the studied group were 52 ± 36 h and 144 ± 126 d, respectively, the collective number of worked hours amounted to 83,460 h Compared to other fields of study students of medicine engaged in volunteering for more hours and for more days The main tasks performed by the surveyed group included triage, servicing call-centers for patients and working at the admission ward, hospital clinics, emergency departments and diagnostic labs The level of fear at the beginning of volunteering was relatively low in the studied group and did not increase over the course The majority of students received positive feedback from families, friends, patients and healthcare workers, revealed a high level of satisfaction from volunteering (also when experiencing COVID-19-related prejudice), while gaining professional experience and a sense of giving real aid were among the most frequently indicated benefits The results of the present study demonstrate that although medical students are not essential workers in response to the COVID-19 pandemic, they can be of real assistance to healthcare systems during times of emergency, and should be considered as such in the future in case such a need arises again

Social vulnerability and short-term disaster assistance in the United States

Abstract: Short-term disaster assistance is an important component of federal disaster response in the United States, providing over $63 billion from 2007 to 2016. Though assistance programs are designed to facilitate the return to basic living conditions, ambiguity surrounds their relationship with socially vulnerable populations who are most likely to require external aid. This study explores the spatial and statistical association between short-term disaster assistance and social vulnerability across the contiguous US. Analysis using bivariate Local Indicators of Spatial Association revealed places with high levels of both assistance and social vulnerability to be clustered in the southeastern United States, as were those with low assistance and high social vulnerability. Overall, places with high social vulnerability were predominantly rural. Based on multivariate regression analysis, dollar damage was the major determinant of allocated assistance for homeowners, but was not explanatory for renters. Indicators of race were associated with lower levels of assistance to homeowners in places where assistance was otherwise high. Among renters, indicators associated with increased coping capacities were associated with greater levels of assistance in places with low allocations of assistance disbursement. Our findings indicate disaster assistance may be underserving some places with more socially vulnerable populations. We recommend that social vulnerability should be explicitly considered in the allocation of assistance to improve social equity in short-term assistance programs.

The role of social capital in disaster resilience in remote communities after the 2015 Nepal earthquake

Abstract: Social capital is widely regarded as a key element in recovery from and resilience to disasters Yet, little attention has been paid to the specificities of what supports or undermines remote rural communities' social capital in disasters Here, we examine how bonding, bridging, and linking social capital operated after the 2015 earthquake in three remote Nepali communities of Sindhupalchok and Gorkha Districts, which have varying degrees of access to infrastructure, relief and recovery programmes We draw on community-based qualitative research conducted in 2018 (including data from Participatory Videos, Focus Group Discussions and Key Informant Interviews) to show how different forms of social capital ‘matter’ more in different phases of recovery Immediately after the earthquake, high levels of bonding and bridging social capital among residents reduced barriers to collective action and helped efforts to rescue and support affected individuals This dissipated, however, once external relief arrived Already-marginalised groups with low social capital of all types were less able to access relief items and funding for rebuilding compared with those of higher social status or with political links Pre-existing socio-cultural inequalities, including those driven by weak bonding relationships in families, gender inequalities and the remoteness of villages, further undermined communities' social capital and their resilience to the earthquake Disaster relief programmes should target women and the elderly to improve the resilience of marginalised communities to future disasters For long-term resilience, disaster programmes should consider social capital in terms of power and pre-existing inequalities, so that linking capital would not just serve elite groups

Twitter for disaster relief through sentiment analysis for COVID-19 and natural hazard crises

Abstract: In emergencies and disasters, large numbers of people require basic needs and medical attention In such situations, online social media comes as a possible solution to aid the current disaster management methods In this paper, supervised learning approaches are compared for the multi-class classification of Twitter data A careful setting of Multilayer Perceptron (MLP) network layers and the optimizer has shown promising results for classification of tweets into three categories ie ‘resource needs’, ‘resource availability’, and ‘others’ being neutral and of no useful information Public data of Nepal Earthquake (2015) and Italy Earthquake (2016) have been used for training and validation of the models, and original COVID-19 data is acquired, annotated, and used for testing Detailed data analysis of tweets collected during different disasters has also been incorporated in the paper The proposed model has been able to achieve 83% classification accuracy on the original COVID-19 dataset Local Interpretable Model-Agnostic Explanations (LIME) is used to explain the behavior and shortcomings model on COVID-19 data This paper provides a simple choice for real-world applications and a good starting point for future research

What influences panic buying behaviour? A model based on dual-system theory and stimulus-organism-response framework

Abstract: Panic buying has been observed across many regions during the COVID-19 pandemic which greatly disrupts supply chains and market economies. The determinants of panic buying, upon being identified, can be applied to control the escalation of panic buying behaviour that is highly detrimental to societies. This research aims to synthesise the dual-system theory and stimulus-organism-response framework to investigate into the causes of panic buying. Structural equation modelling is employed to analyse data collected from 508 residents in Singapore. The results reveal that panic buying can be explained as a response to both environmental stimuli and reflective thinking. Specifically, perceived susceptibility and perceived severity of a pandemic event as well as social influence and social norm can stimulate consumers' perceptions of scarcity and affective response, which in turn leads to the impulsive decision of panic buying; meanwhile, a rational reflection which is operationalised by perceived lack of control also influences panic buying. Furthermore, the perceived lack of control positively moderates the effect of affective response on panic buying. Theoretically, this research provides a unique explanation of panic buying. The findings also provide managerial implications on dealing with panic buying in response to disasters such as a health crisis.

The capacity of grey infrastructure in urban flood management: A comprehensive analysis of grey infrastructure and the green-grey approach

Abstract: Urban inundation management should take various forms to deal with different flood situations, rapid population growth, and climate change. Research on the advantages and disadvantages of grey infrastructure, such as deep tunnels and pipe networks, is necessary to apply hybrid green-grey infrastructure scientifically and efficiently. A novel urban flood model was applied in a highly urbanized region to evaluate the grey infrastructure. Green-grey approaches were also compared. The results showed that deep tunnels decreased the peak flood volume and shortened the time of peak flow. Pipe network improvements decreased the peak flood volume but did not affect the time of peak flow. However, the deep tunnels could not mitigate local inundations caused by insufficient sewer pipe capacity, whereas pipe network improvements could not mitigate inundations caused by backwater flooding. Moreover, pipe network improvements tended to generate new downstream inundation hot-spots. Green-grey infrastructures performed better than grey infrastructures for urban flood reduction. A hybrid green-grey approach was recommended. Suitable combinations of green-grey infrastructures improve the stability and effectiveness of urban flood management.

How computer vision can facilitate flood management: A systematic review

Abstract: Better prediction and monitoring of flood events are key factors contributing to the reduction of their impact on local communities and infrastructure assets. Flood management involves successive phases characterized by specific types of assessments and interventions. Due to technological advances, computer vision plays an increasing role in flood monitoring, modeling and awareness. However, there is a lack of systemic analysis of computer vision's relative adequacy to specific needs associated with successive flood management phases. This article presents a systematic review of relevant literature and proposes a need-based evaluation of these use-cases. Finally, the article highlights future areas of research in this domain.

Japanese social capital and social vulnerability indices: Measuring drivers of community resilience 2000–2017

Abstract: Recently, scholars have turned to publicly available data to measure the resources and vulnerability of communities in the face of disasters [1,2]. However, when measuring community resilience to climate change, custom surveys of social capital are often costly or unfeasible to conduct for every community in a country. Despite suffering numerous disasters in the last thirty years, Japanese disaster scholarship lacks municipality-level measures of social capital and social vulnerability. This study uses publicly available data to develop new bonding, bridging, and linking social capital indices, paired with a new social vulnerability index, available for each of Japan's 1741 municipalities, using principal component analysis and validation techniques. Scholars and policymakers can directly apply these indices to evaluate the social capital or vulnerability of specific communities, compare across multiple communities, model their effect of outcomes, and better prepare for future disasters.

Bend or Break? Small Business Survival and Strategies during the COVID-19 Shock

Abstract: This manuscript studies the impact of the exogenous COVID-19 pandemic shock on small businesses in the United States. We provide early evidence on how small business owners were affected by COVID-19 and the implementation of the Coronavirus Aid, Relief, and Economic Security (CARES) Act. We collected online survey data from a national sample of 463 small business owners across the United States. The survey was conducted in June 2020, eight weeks after the passage of the CARES Act and the Paycheck Protection Program and Health Care Enhancement Act. The survey data include information about business characteristics, financial well-being, current response to the crisis, beliefs about the future of their business survival, and the business-owning family demographic information. There are three main themes that emerge from the results. First, drivers of income loss were not necessarily associated with time to recovery. Second, businesses that were undercapitalized were more likely to suffer higher income loss, longer time to recovery, and less likely to be resilient. Resilient was operationalized as a scale merging perceived success, potential for growth, and perceived profitability. Third, business model changes were necessary due to the pandemic but not all adaptive strategies led to better business outcomes. The results from this research study will lead to a better understanding of key vulnerabilities and adjustments that small businesses make to fully recover from economic shocks.

Public health agencies outreach through Instagram during the COVID-19 pandemic: Crisis and Emergency Risk Communication perspective

Abstract: Background Governmental and non-governmental institutions increasingly use social media as a strategic tool for public outreach. Global spread, promptness, and dialogic potentials make these platforms ideal for public health monitoring and emergency communication in crises such as COVID-19. Objective Drawing on the Crisis and Emergency Risk Communication framework, we sought to examine how leading health organizations use Instagram for communicating and engaging during the COVID-19 pandemic. Methods We manually retrieved Instagram posts together with relevant metadata of four health organizations (WHO, CDC, IFRC, and NHS) shared between January 1, 2020, and April 30, 2020. Two coders manually coded the analytical sample of 269 posts related to COVID-19 on dimensions including content theme, gender depiction, person portrayal, and image type. We further analyzed engagement indices associated with the coded dimensions. Results The CDC and WHO were the most active of all the assessed organizations with respect to the number of posts, reach, and engagement indices. Most of the posts were about personal preventive measures and mitigation, general advisory and vigilance, and showing gratitude and resilience. An overwhelming level of engagement was observed for posts representing celebrity, clarification, and infographics. Conclusions Instagram can be an effective tool for health organizations to convey their messages during crisis communication, notably through celebrity involvement, clarification posts, and the use of infographics. There is much opportunity to strengthen the role of health organizations in countering misinformation on social media by providing accurate information, directing users to credible sources, and serving as a fact-check for false information.

Hospital evacuation modelling: A critical literature review on current knowledge and research gaps

Abstract: The frequency and intensity of both human-made and natural disasters are predicted to increase, and hospitals play a critically important role in reducing injury and mortality rates. However, there is increasing evidence that many hospitals are vulnerable to disasters, and more effective strategies are needed to enable the safe evacuation of patients to alternative healthcare centres. Transport planning is central to this process, but there has been no systematic and critical review of the research on this critically important challenge. This means there is no collective synthesis of this literature for hospital managers, policymakers, and researchers to refer to in addressing these important vulnerabilities. This paper reports the findings of a critical literature review to contribute practical insights for health facilities planning and management decision-making in a context where both the likelihood and consequences of natural disasters are increasing in many countries.

Editorial. Risk-Based, Pro-Poor Urban Design and Planning for Tomorrow’s Cities

Abstract: Tomorrow’s Cities is the £20m United Kingdom Research and Innovation (UKRI) Global Challenge Research Fund (GCRF) Urban Disaster Risk Hub. The Hub aims to support the delivery of the United Nation’s Sustainable Development Goals and priorities 1 to 3 of the Sendai Framework for Disaster Risk Reduction (DRR) 2015-2030. We work in four cities: Istanbul, Kathmandu, Nairobi, and Quito. We collaborate with local, national, and global organisations to strengthen disaster risk governance by undertaking integrated, multi-scale, and multi-disciplinary research to better understand natural multi-hazard risks and their drivers. Ongoing rapid urbanisation and urban expansion provide a time-limited opportunity to reduce disaster risk for the marginalised and most vulnerable in tomorrow’s cities. We aim to catalyse and support a transition from crisis management to pro-poor, multihazard risk-informed urban planning and people-centred decision-making in expanding cities worldwide. Tomorrow’s Cities is a fully-functioning, fully-funded international collaboration of communities, governance organisations, researchers, and risk professionals. We are developing our Phase 2 programme planned for 2021-24, which will build on the Phase 1 research and partnerships forged since our inception in early 2019. We seek global partners to co-produce and implement a new approach to risk reduction, through risksensitive design of tomorrow’s cities.

Enhancing urban flood resilience: A holistic framework incorporating historic worst flood to Yangtze River Delta, China

Abstract: Rapid urbanization and climate change have increased the risk of urban flooding, causing massive infrastructure and human losses. The concept of resilience proposes new solutions to manage flood disaster. An urban flood resilience evaluation framework considering the flood disaster cycle of actual historic flood event and objective physical-socio-economic status is necessary for future flood mitigation. This paper proposes a holistic evaluation framework for evaluating urban flood resilience with VIKOR and Grey Relational Analysis (GRA) method. The proposed framework consists of indicators of resistance, coping, recovery and adaptation capacity of resilience for three stages of the flood disaster cycle, namely pre, during and post-flood. The framework has been applied to Yangtze River Delta (YRD) consisting 27 cities in China. Following a rigorous analysis, the cities are ranked and mapped, among which Nanjing stands out to be the first, whereas the entire region presents a moderate level of urban flood resilience varying from city to city. The detailed comparison with sensitivity analysis of resilience at regional, provincial and city level suggests a better resilience in pre-flood stage than post-flood stage. Finally, practical recommendations to regional and local level are provided for further flood mitigation and resilience improvement. The proposed framework is generalizable and useful to develop flood related standards, establish benchmarks, perform evaluation at regional, provincial and city levels across China and other parts of the world.

The bright and dark sides of social media usage during the COVID-19 pandemic: Survey evidence from Japan

Abstract: The spread of misinformation on the internet regarding the COVID-19 pandemic, such as unproven or fake cures, has been a serious concern. However, the extent to which social media usage affects individuals' health behavior, particularly when reliable information is scarce, is not well understood. This study evaluates the impact of social media usage on individuals’ responses to the COVID-19 pandemic, such as demand for necessities and social distancing. We conduct an original online survey of 1804 Japanese respondents in March 2020. Japan is suitable because it confirmed COVID-19 cases earlier than most other countries. Scientific evidence about the coronavirus and protective measures was scarce in the initial pandemic phase, despite the spread of unconfirmed rumors. Our analysis focuses on the usage of Twitter, Facebook, and Instagram. We use the entropy balancing method to control for heterogeneity in observed characteristics between social media users and non-users. The results show that while users are more likely to maintain social distancing practices, they are also more likely to take measures whose reliability is not scientifically confirmed, such as eating fermented soybeans. Although previous studies emphasize the negative effects of social media, our results suggest that it has both bright and dark sides.

A Collaborative Serious Game for Water Resources Planning and Hazard Mitigation

Abstract: Hydrological hazards lead to a broad range of socio-economic and environmental risks. The development of a resilient community and risk reduction rely on the adoption of holistic watershed master planning whereby the adaptation options consider the risk associated with individual or multiple hydrological hazards occurring simultaneously at a specific location. Such planning approaches pose multiple challenges for decision-makers including the access and manipulation of high-volume high-variety data. Moreover, modern planning approaches involve the watershed community in decision-making, hence, these approaches have to include elements of social learning and relationship building. This study introduces a serious gaming framework to assist stakeholders in the decision-making process for water resources planning and hazard mitigation. A Multi-Hazard Tournament (MHT) is described that allows the members of a watershed community to evaluate various adaptation options to develop mitigation strategies for multiple water-related hazards (e.g. flood, drought, and water pollution). The tournament offers a competitive and gamified setting for collaborative hazard risk assessment to minimize damages to water quality, riparian habitat, water resources availability, vulnerable populations, and infrastructure. A web-based decision support tool is developed to provide interactive interfaces and engaging visualizations and serve as a one-stop platform to investigate the challenges presented for each damage center within its geographic context and to analyze the cost/benefit relationship for potential hazard mitigation strategies with dynamic analytics tools. Finally, a case study is presented to evaluate the effectiveness of MHT in increasing collective awareness and conceptual and practical understanding of water-related hazards and mitigation strategies.

Landslide risk index map at the municipal scale for Costa Rica

Abstract: Landslides are a common natural hazard worldwide with greater socioeconomic impacts in developing and tropical countries. In Central America and Costa Rica, this phenomenon is mainly triggered by seismicity and extraordinary rainfall. In order to portray the damaging landslides, that caused human and material losses in Costa Rica, DesInventar disaster database was used to analyze damaging landslides reports from 1970 to 2018. Moreover, different generalized linear models (GLM) were performed to analyze the landslide hazard, vulnerability and risk in all the municipalities of the country. An Akaike Information Criterion (AIC) backward selection was used to contrast and determine the best hazard and vulnerability models. From a total of ten variables, terrain ruggedness index, 5 year intensity-duration-frequency precipitation curves and earthquakes distribution determined the landslide hazard. Otherwise, population, municipality area and Social Development Index are the most suitable variables to explain the landslide vulnerability. Subsequently, the multiplication between alternative landslide hazard and vulnerability indexes produced the risk index. Consequently, the highest risk values were obtained for large and rural municipalities (Perez Zeledon, San Carlos, and Turrialba) as well as for densely populated and urban units (Alajuela, Desamparados, and Cartago. Results are critical for disaster risk reduction public institutions and academic stakeholders. Therefore, this methodology could be an interesting opportunity for different tropical and developing countries to achieve national or regional analyses of the most important risk component in each municipality and implement risk reduction strategies adapted for each municipality characteristics.

An Integrated Flood Risk Assessment and Mitigation Framework: A Case Study for Middle Cedar River Basin, Iowa, US

Abstract: Property buyout is one of the most frequently preferred flood mitigation applications by decision-makers for long-term risk reduction. Due to its high-level funding requirements as a mitigation solution, it requires extensive benefits and costs analysis for the selected region. Many communities in the State of Iowa experienced extreme flood events (i.e., 1993, 2008, 2014, 2019), which resulted in a heavy economic impact over the last couple of decades. Nearly 3000 property acquisitions have been made between 2007 and 2017 using federal programs. This study presents a web-based Flood Risk Assessment and Mitigation Environment (FRAME), which provides visual data analytics capabilities to analyze property and community level benefit-cost analysis for property acquisitions. The FRAME allows users to explore and visualize historical mitigation projects and buyouts, and evaluate avoided damages for their communities. As a case study, a detailed benefit-cost analysis of historical property buyouts and direct losses of existing properties in the Middle Cedar watershed in Iowa is studied using stream gauge data from the United States Geological Survey (USGS). Projected stream gauge datasets, which are outputs of two climate scenarios (A1FI-fossil intensive and A2-low emission), are also utilized to assess future avoided losses for acquisitions and possible direct economic losses for existing properties. Case study results indicate that the average benefit-cost ratio (BCR) for buyouts in the studied region is around 0.86. Nearly half of the buyouts reached 4.72 BCR in low emission and 6.3 BCR in fossil intensive climate projections if future floods are considered.

Livelihood risk and adaptation strategies of farmers in earthquake hazard threatened areas: Evidence from sichuan province, China

Abstract: It is the final year for China to meet its targets for the 2020 deadline in its battle against poverty. Exploring the livelihood risks faced by farmers and their livelihood adaptation strategies in areas where disaster and poverty are intertwined can provide useful insights for the formulation and implementation of government policies for alleviating poverty. Based on survey data from 327 households in the Wenchuan and Lushan counties of Sichuan, China, this study systematically analyzed the four types of livelihood risks faced by farmers and six types of livelihood adaptation strategies they adopted. Multinomial logistic regression models were constructed to explore the correlation between the livelihood risks and the livelihood adaptation strategies. The results showed that: (1) Among the four livelihood risks faced by farmers, social risks were the largest and health risks were the smallest. (2) Among the six livelihood adaptation strategies adopted by farmers, borrowing money and loans was the most, while choosing to wait for government relief was the least. (3) When faced with health risks, farmers preferred to work outside of the home; when faced with environmental risks, farmers preferred to wait for government relief; when faced with financial risks, there were no significant differences between the six livelihood adaptation strategies chosen by farmers; when faced with social risks, farmers preferred to draw on their savings to survive.

Measuring community disaster resilience at local levels: An adaptable resilience framework

Abstract: Decision makers, practitioners and community members need to assess the disaster resilience of their communities and to understand better the risks they face from natural hazards. There is a lack of consensus on what resilience means and how it can be measured as each stakeholder potentially brings a different perspective to understanding community disaster resilience. The paper will identify the key features and characteristics of Community Disaster Resilience (CDR) frameworks from the literature to develop a resilience framework that can be adapted and customised according to stakeholder needs. The paper used a 5-step process to develop an adaptable CDR framework. First, a review of 36 resilience frameworks was conducted to identify key features and characteristics of resilience frameworks. In Steps 2 and 3, a matrix of indicators and measures was populated by resilience dimensions covered in the current CDR literature reviewed. Subsequently, the indicators were sorted for similarities and duplicates were removed. Finally, they were clustered by six critical resilience dimensions (i.e. Physical, Health, Economic, Environmental, Social and Governance) into a library of 86 resilience indicators (composed of 360 measures) that can be used to operationalize a CDR framework according to the needs of the stakeholders. The review indicated that majority of the articles selected use objective approaches to measure resilience showing a gap for more frameworks using subjective, or participatory, approaches to measuring community resilience. An adaptable CDR framework may make resilience assessment more grounded in local stakeholder perspectives and lead to a better understanding of community resilience.

The information-seeking behavior and levels of knowledge, precaution, and fear of college students in Iloilo, Philippines amidst the COVID-19 pandemic

Abstract: COVID-19 pandemic is devastating the health, social, and economic well-being of citizens worldwide. The high rates of morbidity and mortality and the absence of vaccines cause fear among the people regardless of age, gender, or social status. People's fear is heightened by misinformation spread across all media types, especially on social media. Filipino college students are one of the top Internet users worldwide and are very active in social media. Hence they are very prone to misinformation. This paper aims to ascertain the levels of knowledge, precaution, and fear of COVID-19 of the college students in Iloilo, Philippines, and determine the effects of their information-seeking behavior on the variables above. This paper is a cross-sectional survey that used a qualitative-quantitative method and snowball sampling technique. Data were gathered among 228 college students using an online survey instrument a few months after the pandemic began. College students were knowledgeable of the basic facts about the highly infectious COVID-19. However, the majority were inclined to believe the myths and misinformation regarding the pandemic. Television was the primary, most believable, and preferred source when seeking information. The Internet as a preferred source of information was significantly associated with a high level of knowledge. In contrast, the information sourced from interpersonal channels were found to make college students very cautious. The local presence of COVID-19 cases had caused college students to fear, likely exacerbated by the plethora of information about the pandemic, mostly from Facebook. This is the first study conducted on the effects of the information-seeking behavior on the levels of knowledge, precaution, and fear of COVID-19 of the college students in Iloilo, Philippines.

A mixed approach for urban flood prediction using Machine Learning and GIS

Abstract: Extreme weather conditions, as one of many effects of climate change, is expected to increase the magnitude and frequency of environmental disasters. In parallel, urban centres are also expected to grow significantly in the next years, making necessary to implement the adequate mechanisms to tackle such threats, more specifically flooding. This project aims to develop a flood prediction system using a combination of Machine Learning classifiers along with GIS techniques to be used as an effective tool for urban management and resilience planning. This approach can establish sensible factors and risk indices for the occurrence of floods at the city level, which could be instrumental for outlining a long-term strategy for Smart Cities. The most performant Machine Learning model was a Random Forest, with a Matthew's Correlation Coefficient of 0.77 and an Accuracy of 0.96. To support and extend the capabilities of the Machine Learning model, a GIS model was developed to find areas with higher likelihood of being flooded under critical weather conditions. Therefore, hot spots were defined for the entire city given the observed flood history. The scores obtained from the Random Forest model and the Hot Spot analysis were then combined to create a flood risk index.

A two level interval valued neutrosophic AHP integrated TOPSIS methodology for post-earthquake fire risk assessment: An application for Istanbul

Abstract: Earthquakes are the leading natural disasters that seriously affect human life. Furthermore, earthquakes are natural disasters that have the ability to trigger a second disaster in addition to the damages they cause. From this point of view, post-earthquake fires are defined as the one of the most dangerous secondary disasters after an earthquake and often cause even more serious dangers. For this reason, government officials and relevant decision-makers should effectively determine post-earthquake fire risks and take necessary precautions. In this study, we consider the problem of determining the fire risk after an earthquake as a multi-criteria decision problem and present a two-level framework for risk assessment. The main and sub-criteria are determined by a detailed literature review and Modified Delphi method is employed to gain and consolidate expert opinions. Firstly, the importance weights of the criteria for post-earthquake fire risk problem are determined by the interval valued neutrosophic-Analytical Hierarchy Process (IVN-AHP) methodology. Then, interval valued neutrosophic TOPSIS (IVN-TOPSIS) method is used to rank the districts in Anatolian side of Istanbul according to their post-earthquake fire risks. The proposed risk assessment methodology is utilized with real life data to determine the most risky districts of Istanbul, Turkey. The result of proposed methodology is tested and validated with sensitivity analysis. A comparative analysis also is conducted to further validate the robustness and effectiveness of the proposed methodology. The proposed integrated methodology is intended to be a useful tool for risk assessment and to provide decision makers with a reliable assessment.