Natural Disaster Risk Research Articles

The risk impacts of global natural and technological disasters

This research develops an expected risk model and estimates risk impacts of single or multiple disasters in terms of human fatality, injury, affected, and economic damage for nearly 200 nations. Both natural hazards, including geophysical, meteorological, hydrological, and technological disasters, such as industrial, transport, and structural, are considered. Relevant measures of expected risk, such as standard deviation, coefficient of variance, range, and rank are also calculated to assess a country's overall risk. Social-economic-physical factors from the World Development Index developed by the United Nations (UN) are then regressed with occurrences and risks of natural and tech disasters to seek plausible associations. The results show that (1) the model performs reasonably well in fitting observed and modeled risks and risk impacts, relatively better for natural disaster and affected people and economic damage; (2) while natural disasters are far more risky than tech ones in total risk impacts; specific risks for subgroups of natural or tech disasters vary widely in magnitude and by country; (3) high natural and/or tech risks concentrate in a small number of countries, such as China, India, Bangladesh in Asia; U.S., Mexico, Canada in North America; Turkey, Russia, France, Germany in Europe, and Algeria, Egypt, and Ethiopia in Africa, which are relatively large in population, fast in development, or advanced in industrialization; yet (4) high risk deviations per unit risk impacts reveal that many small, developing, and tech backward countries need to prepare even more for both natural and tech disasters. Finally, (5) while many country-level development factors, together with disaster occurrence, are statistically significant, only some can provide weak predictability of disaster risk impacts under robust regression. The research findings provide useful risk references to countries for resilient national policies for disaster preparation, mitigation, and recovery.

Digital Transformation and Risk Mitigation in Emerging Insurance Markets: A Comparative Analysis Between China and Serbia

Recent trends indicate a deceleration in the growth of insurance markets in developed countries, whereas emerging insurance markets, predominantly in Southeast Asia, and led by China, demonstrate a remarkable increase in total gross premiums. This contrast, largely attributed to digitalization of insurance operations, underscores the need for understanding and integrating technological innovations within the insurance industry. Faced with emerging risks such as climate change, natural disasters, epidemics, pandemics, and cyber threats, insurance companies must implement proactive risk prevention strategies alongside enhancing their risk mitigation instruments. Utilizing comprehensive, country-specific alert systems can aid in evaluating risk profiles and managing the emergent risks more efficiently. While Serbia's efforts towards digital transformation in the insurance sector are still budding, there remains a significant potential for growth and introduction of innovative insurance products. However, the lack of adequate preventive measures against risks such as natural disasters has hampered its full development potential in sectors like agriculture and tourism. On the other hand, China's approach to managing natural disaster risks through robust institutional support and an innovative insurance market offers valuable insights. This study aims to highlight the significant challenges posed by modern trends in the insurance industry, emphasizing the case of China, and suggesting ways for insurance companies to respond to these challenges. Moreover, it assesses the readiness of the Serbian insurance market in managing natural disaster risks and provides a roadmap for the integration of technology within the insurance sector.

STRATEGIES AND WAYS TO DEAL WITH NATURAL DISASTERS

Human societies in general have witnessed a great development in the field of strategies for managing disasters, and the strategy here has become a major means of control and a manifestation of strength and sovereignty, and a main focus for various human phenomena and social, political and economic issues until it has become the most dangerous tools of the age, and in light of this tremendous development the world witnessed what is known as science Natural disaster management, which is one of the required and necessary sciences and disciplines, because the disaster situation calls for distinct awareness of the treatments. Therefore, the problems and issues of natural disasters have become extended and deep issues that exist in multiple forms at all local and global levels. Which is a shared responsibility, and through this study, a change is also made in the behavior and habits of the individual in the face of these natural disasters, all through a well-established strategy. Through this, the researcher came up with the research objectives, which are as follows: 1. Knowing the role played by a strategy in managing natural disasters, and the possibility of improving its performance in this field. 2. Finding out the problems that face the work of the strategy and the limitation of doing a job in dealing with natural disasters. 3. Activating and employing the strategy to educate the community about the risks of natural and other disasters. 4. Determine the scientific concepts of natural disaster management, and how to apply them in the field for a decisive and effective response. 5. Highlighting the vital and important role of the strategy before, during and after natural disasters. From this point of view, the researcher made the importance of the research as follows: 1. It stems from the importance of raising awareness of the seriousness of natural disasters that threaten human life in any place and time. 2. Clarify the role of the strategy in all stages of the disaster, whether before, during or after its occurrence. 3. Predicting the occurrence of disasters and fully qualifying them to confront them. 4. The importance of the research stems from finding and establishing modern means through which natural disasters can be avoided. 5. Highlighting a strategic role in reducing natural disasters that threaten the environment, including living organisms.The researcher used the descriptive and analytical method: The researcher also focused on addressing the role of strategy in managing natural disasters, as an already existing situation, as disasters are among the phenomena that threaten the life of humanity in general, so it must be analyzed carefully based on the principle of safety, and not to be ignored, because this will have a negative impact on people's lives. Hence, the researcher touched on putting the research mechanism in one chapter, and three topics, as follows: Chapter One: Strategies and ways to deal with natural disasters The first topic: identifying natural disasters, and pre-planning for them. The second topic: the strategy and its role in educating the community about the dangers of natural disasters The third topic: mechanisms for dealing with natural disasters

The impact of natural disaster risk on the return of agricultural futures

This paper constructs a natural disaster risk index based on natural disaster keywords from news media that can reflect weather, crop diseases, epidemics, and other threats to agricultural prices in China. We then examine the response of agricultural futures returns to the change in natural disaster risk. We find that natural disaster risk has a significant positive impact on soybean, wheat, corn, and sugar futures returns during normal and bullish market conditions. We further investigate the moderating effect of policy intervention and show that the marginal effect of natural disaster risk on soybean futures returns (but not other agricultural futures) is influenced by policy intervention. These results indicate that the adjustment of natural disaster risk assessment will benefit portfolio management.

STUDY AND APPLICATION OF FLOOD CONTROL RISK TREND ANALYSIS MODEL

Abstract. In order to analyze the comprehensive risks of natural disasters quantitatively and improve the accuracy of natural disaster management and control, this paper expands the F indicator, Forecast, which is about real-time monitoring and early warning data of natural disasters, and forms the flood control risk trend analysis model framework based on PSR. The framework is named FPSR, i.e. Forecast-Pressure-State-Response, composed of static data and dynamic data. By establishing the four-level index system of flood control risk trend analysis in Fangshan District of Beijing, screening factors, and using analytic hierarchy process, AHP, and experts scoring to determine the weights of each factor, it constructs the flood control risk trend analysis model, FCRTAM. At last, using the real-time monitoring and early warning data of natural disasters in Beijing and the information such as disaster-causing factors, historical natural disasters, major hidden dangers, disaster-bearing bodies, disaster reduction resources (capacities), etc., from National Natural Disaster Comprehensive Risk Census in Fangshan, it analyzes the flood control situation of each town in Fangshan. The results show that the results flood control risk index calculated according to FCRTAM is basically consistent with the actual flood control situation of the towns in Fangshan, and can provide theoretical basis for flood control comprehensive risk trend analysis and the decision-making of disaster prevention and reduction in Fangshan District, which has high use value.

Is mandatory seller disclosure of flood risk necessary? A Brisbane, Australia, case study

PurposeInformation on the impact of flooding is fundamental to mitigating flood risk in residential property. This paper aims to provide insight into the seller disclosure of flood risk and buyer behaviour in the absence of mandated seller disclosure.Design/methodology/approachThis paper adopts a case study approach to critically evaluate the matrix of flood information available for buyers purchasing residential property in Brisbane, Queensland. This paper uses big data analytic techniques to extract and analyse internet data from online seller agents and buyer platforms to gain an understanding of buyer awareness and consideration of flood risk in the residential property market.FindingsAnalysis of property marketing data demonstrates that seller agents voluntarily disclose flood impact only in periods where a flooding event is anticipated and is limited to asserting a property is free of flood risk. Analysis of buyer commentary demonstrates that buyers are either unaware of flood information or are discounting the risk of flood in favour of other property and locational attributes when selecting residential property.Practical implicationsThis research suggests that improved and accessible government-provided flood mapping tools are not enhancing buyers’ understanding and awareness of flood risk. Accordingly, it is recommended that mandatory disclosure be introduced in Queensland so that buyers are more able to manage risk and investment decisions before the purchase of residential property.Originality/valueThis paper contributes to existing literature on raising community awareness and understanding of natural disaster risks and makes a further contribution in identifying mandatory disclosure as a mechanism to highlight the risk of flooding and inform residential property purchasers.

ASSESSING THE ROLE OF SUSTAINABLE DEVELOPMENT IN MITIGATING THE ISSUE OF GLOBAL WARMING

The relationship between Sustainable Development (SD) and Global Warming (GW) is a critical issue that has implications for the future of our planet. This research paper explores the ways in which SD can help address GW and the ways in which GW can impact SD. The paper argues that SD can help mitigate the negative impacts of Climate Change (CC) by promoting the use of renewable energy, reducing waste and pollution, and promoting sustainable land use practices. However, achieving SD in the context of GW requires a holistic approach that balances economic, social, and environmental concerns. Furthermore, the paper discusses the ways in which GW can impact SD by disrupting ecosystems, reducing the availability of natural resources, and increasing the risk of natural disasters. It also highlights the importance of adopting policies and practices that address the root causes of GW, while promoting SD practices that benefit all people and the planet. The research paper concludes that achieving SD and addressing GW are two interconnected issues that must be prioritized to ensure a healthy and prosperous future for all people and the planet. It calls for urgent action and collaboration among governments, businesses, and individuals to adopt SD practices and reduce Greenhouse Gas Emissions (GHGE).

The Most Vulnerable Populations: Exploring the FEMA National Risk Index by Racial/Ethnic Group

Introduction:The US Federal Emergency Management Agency (FEMA) has created a publicly available National Risk Index (NRI) using natural disaster and community risk factor data to provide numeric and visual representations of communities’ disaster risk. Of particular interest is the index’s use of the social vulnerability and community resilience variables. This study’s purpose was to identify and explore differences in vulnerability and resilience based on racial demographic data at the census tract level. By identifying communities at the highest risk, we can address modifiable risk factors to improve natural disaster outcomes for vulnerable populations.Method:This project used merged data from the US Census Bureau’s 2019 American Community Survey and source data for the FEMA NRI. Using Microsoft Excel, we created scatter plots of the social vulnerability and community resilience variables by census tract and predominant racial group, and calculated the mean, standard deviation, and statistical difference between those variables by race.Results:In census tracts where Native Americans made up ≥50% of the population:1) There was a positive linear association between percent of Native Americans and increased social vulnerability.2) The average social vulnerability score (ASV) was significantly higher (ASV = 55.74) than for predominantly White tracts (ASV = 31.43) (p < 0.001).3) The average risk score (ARS) was significantly higher (ARS = 30.18) compared to predominantly White tracts (ARS = 16.98) (p < 0.001).4) The average resilience index score (ARIS) was significantly lower (ARIS = 51.83) than predominantly White tracts (ARIS = 55.08) (p < 0.001).Conclusion:Results show that census tracts with predominantly Native American populations face significantly higher natural disaster risk and social vulnerability, and have significantly lower resilience scores compared to predominantly White tracts. Using modifiable factors to improve community resilience and decrease social vulnerability, the US can better protect communities at high natural disaster risk.

Evidence-based disaster risk assessment in Southeast Asian countries

This paper aims to provide an overview of the risks and trends of natural disasters in Southeast Asian countries and discuss necessary future countermeasures. Quantitative analysis of natural disasters was performed using data collected from EM-DAT. The disaster risk assessment for countries and disaster types was conducted using a frequency-impact risk matrix and risk ranking identification, which is a novel approach based on actual disaster data. The findings suggest that Indonesia, the Philippines, and Thailand face the highest mortality, affected population, and economic damage risks, respectively. Moreover, the study reveals that Southeast Asian countries are vulnerable to climate-related disasters. This risk assessment gives us a comprehensive understanding of natural disaster risks in Southeast Asian countries, enabling us to identify the most vulnerable countries in the region and determine the most significant disaster-type countermeasures for each country.

Flood vulnerability analysis using different aggregation frameworks across watersheds of Ardabil province, northwestern Iran

Flood-vulnerable-area selection could be interpreted as an intricate multiple-criteria decision-making (MCDM) problem. Lacking systematic flood vulnerability maps in Iran usually leaves the National Disaster Management Organization and provincial authorities to rely on limited knowledge to inform their emergency actions. Therefore, the present study was conducted as pioneer research in flood problematization for solving disaster issues that have not been adequately considered in the country. To obtain a more trustworthy and safer decision, a complex set of multivariate flood vulnerability assessments was done for 26 watersheds of Ardabil province using the novel-ensemble MCDM frameworks. Towards this, the complex proportional assessment of alternatives (COPRAS) and the UK Department for International Development (DFID) approach was integrated with widely used weighting methods of Shannon's entropy (Entropy) and analytical hierarchy process (AHP). Finally, four different aggregation frameworks of COPRAS-Entropy, COPRAS-AHP, DFID-Entropy, and DFID-AHP were developed and compared based on 19 criteria and three factors (exposure, sensitivity, and resilience). The flood vulnerability was obtained as 0.70 ± 0.13, 0.66 ± 0.18, 0.17 ± 0.10, and 0.20 ± 0.15, respectively, based on COPRAS-Entropy, COPRAS-AHP, DFID-Entropy, and DFID-AHP. As can be seen, higher values were estimated according to COPRAS-based frameworks rather than DFID-based frameworks, except for resilience. The top-ranked watersheds in terms of exposure (i.e., Mashiran) and sensitivity (i.e., Samian) produced by aggregation frameworks match entirely. However, the top-ranked watersheds based on resilience and flood vulnerability slightly mismatch among the used aggregation's frameworks. These findings help the responsible authorities for natural disaster risk reduction to promote sustainable development processes and equitable access to financial resources.

Disasters and corruption: An Empirical Analysis of 16 countries from Asia and the Middle East

Disasters and disaster risks may induce corruption in a country, aggravating the number of deaths and damages while reducing a country's adaptive capacity. Within this framework, our study explores the impact of natural and man-made disasters on corruption. Utilising information from the Political Risk Services (PRS) group and other nationally representative sources, we construct a panel dataset to study the effect of disasters and disaster risk on corruption in 16 Asian and Middle Eastern countries from 1984 to 2018. We estimate the Panel OLS Model, as well as the 2SLS Model. For robustness checks, we run the Panel Ordered Logistic Regression (POLR) and Panel Ordered Probit Regression (POPR) models on the dataset. Our study finds that natural and man-made disasters and disaster risks increase the propensity for corruption in the sample countries. Other key findings of the study reveal that business activities, government stability, national income, and education reduce the likelihood of corruption in our sample countries. Lastly, our findings suggest a higher likelihood of corruption in developing countries. Results from our study support the notion that disasters generate a series of resource windfalls that governments and elites may misuse while increasing petty corruption through bribery and extortion.

A Comprehensive Review of Geospatial Technology Applications in Earthquake Preparedness, Emergency Management, and Damage Assessment

The level of destruction caused by an earthquake depends on a variety of factors, such as magnitude, duration, intensity, time of occurrence, and underlying geological features, which may be mitigated and reduced by the level of preparedness of risk management measures. Geospatial technologies offer a means by which earthquake occurrence can be predicted or foreshadowed; managed in terms of levels of preparation related to land use planning; availability of emergency shelters, medical resources, and food supplies; and assessment of damage and remedial priorities. This literature review paper surveys the geospatial technologies employed in earthquake research and disaster management. The objectives of this review paper are to assess: (1) the role of the range of geospatial data types; (2) the application of geospatial technologies to the stages of an earthquake; (3) the geospatial techniques used in earthquake hazard, vulnerability, and risk analysis; and (4) to discuss the role of geospatial techniques in earthquakes and related disasters. The review covers past, current, and potential earthquake-related applications of geospatial technology, together with the challenges that limit the extent of usefulness and effectiveness. While the focus is mainly on geospatial technology applied to earthquake research and management in practice, it also has validity as a framework for natural disaster risk assessments, emergency management, mitigation, and remediation, in general.

Disaster Risk Regionalization and Prediction of Corn Thrips Combined with Cloud Model: A Case Study of Shandong Province, China

Corn thrips do serious harm to the yield and quality of corn. In this paper, the Shandong Province of China was taken as the study area. Based on the data of the occurrence of corn thrips in Shandong Province, a risk regionalization model was established by using eight indicators under four categories of hazard, sensitivity, vulnerability and the disaster prevention and mitigation capacity of diseases and pests on a monthly time scale. Firstly, the cloud model was introduced to determine the weight of each indicator, and then the risk regionalization of the corn thrips disaster in Shandong Province was carried out using the weighted percentage method, the weighted comprehensive evaluation method and the natural disaster risk index method. Finally, combined with the collected data, the disaster prediction of corn thrip occurrence degree was realized based on multiple linear regression, genetic algorithm optimized back-propagation neural network and genetic algorithm optimized support vector machine methods. The results show that: (1) the risk of Corn thrips disaster is mainly concentrated in the central and western parts of Shandong Province. Heze City is a high-risk area. Liaocheng City, Dezhou City, Jinan City and Weifang City are relatively high-risk areas. (2) By comparing the prediction accuracy of the three models, it was determined that the genetic algorithm optimized support vector machine model has the best effect, with an average accuracy of 79.984%, which is 7.013% and 22.745% higher than that of the multiple linear regression and genetic algorithm optimized back-propagation neural network methods, respectively. The results of this study can provide a scientific basis for fine prevention of corn thrips in Shandong Province.

Determination of Earthquake Insurance Premium Based on Great Physical and Economic Loss Using the Bayesian Method

Indonesia is an area prone to earthquakes because it is traversed by the meeting point of 3 tectonic plates, namely: the Indo-Australian plate, the Eurasian plate and the Pacific plate. An earthquake is an event where the earth vibrates due to a sudden restraint of energy in the earth which is characterized by the breaking of rock layers in the earth's crust. Almost all regions in Indonesia are at risk of being exposed to earthquakes. To anticipate the risk of natural disasters, earthquakes are advised to join the insurance program provided by the insurance company. This study aims to determine earthquake insurance premiums based on large physical and economic losses. The method used is the Bayesian method. This method produces each estimated loss value which is then used to calculate the combined estimated loss value. After that, the combined estimated loss value is used to calculate the premium value. The result of this research is the premium which is calculated based on the principle of expected value and standard deviation principle. The premium resulting from the expected value principle is lower than the premium resulting from the standard deviation principle.

Construction and empirical testing of comprehensive risk evaluation methods from a multi-dimensional risk matrix perspective: taking specific types of natural disasters risk in China as an example

Global climate change, continuous economic development and accelerated urbanisation have increased the frequency and complexity of various incidents. Risk assessment is an important means to deal with different crises and incidents. Based on the systematic analysis of the basic connotation, attribute relationship and main contents of risk assessment, this study extends the traditional two-dimensional risk matrix to multi-dimensional risk matrix. On this basis, this study further proposes four comprehensive risk assessment methods, including total ordinal, weighted average, Euclidean distance and two-norm methods. Empirical research was conducted by selecting three types of natural disasters, regional agro-meteorological disasters, geological disasters and forest fires, in 30 related regions in China between 2010 and 2019. The results indicated that compared to the two-dimensional risk matrix, the multi-dimensional risk matrix can intuitively characterise and compare the all-round risk situation of different regions, periods, types and dimensions, which is more helpful for a systematic analysis and risk comparison. All four evaluation methods proposed in this paper proved able to effectively conduct comprehensive regional risk evaluations with consistent results. However, when considering the fitting coefficient between the results obtained by various evaluation methods and the comprehensive evaluation results, it can be found that the accuracy (i.e. fitting coefficient) of Euclidean distance method is significantly higher than that of the other three methods. In the last 10 years, the temporal order of natural disaster risk in China has been more volatile, with Beijing, Shanxi, Tianjin, Hebei, Liaoning, Ningxia, Zhejiang, Shaanxi, Fujian and Gansu experiencing higher volatility in the ranking of natural disaster risk.

Insights into the complementarity of natural disaster insurance purchases and risk reduction behavior.

While flooding is the costliest natural disaster risk, public-sector investments provide incomplete protection. Moreover, individuals are in general reluctant to voluntarily invest in measures which limit damage costs from natural disasters. The moral hazard hypothesis argues that insured individuals take fewer other preparedness measures based on their assumption that their losses will be covered anyway. Conversely, the advantageous selection hypothesis argues that individuals view insurance and other risk reduction measures as complements. This study offers a comprehensive assessment of factors related to the separate uptake of natural disaster insurance and the flood-proofing of homes as well as why people may take both of these measures together. We use data from a survey conducted in Paris, France, in 2018, after several flood events, for a representative sample of 2976 residents facing different levels of flood risk. We perform both main effects regressions and interaction analyses to reveal that home adaptation to flooding is positively associated with comprehensive insurance coverage, which includes financial protection against natural disasters. Furthermore, actual and perceived risks, as well as awareness of official information on flood risk, are found to explain some of the relationship between home adaptation and comprehensive insurance purchase. We suggest several recommendations to policymakers based on these insights which aim to address insurance coverage gaps and the failure to take disaster risk reduction measures. In particular, groups in socially vulnerable situations may benefit from subsidized insurance, low interest loans, and decision aids to implement costly adaptation measures.

Remote sensing for natural catastrophe risk management: closing the gap between ambition and capability

Remote sensing for natural catastrophe risk management: closing the gap between ambition and capability

Priorities for natural disaster risk reduction in Central America

Priorities for natural disaster risk reduction in Central America

Analysis and Verification of Building Changes Based on Point Clouds from Different Sources and Time Periods

Detecting changes in buildings over time is an important issue in monitoring urban areas, landscape changes, assessing natural disaster risks or updating geospatial databases. Three-dimensional (3D) information derived from dense image matching or laser data can effectively extract changes in buildings. This research proposes an automated method for detecting building changes in urban areas using archival aerial images and LiDAR data. The archival images, dating from 1970 to 1993, were subjected to a dense matching procedure to obtain point clouds. The LiDAR data came from 2006 and 2012. The proposed algorithm is based on height difference-generated nDSM. In addition, morphological filters and criteria considering area size and shape parameters were included. The study was divided into two sections: one concerned the detection of buildings from LiDAR data, an issue that is now widely known and used; the other concerned an attempt at automatic detection from archived aerial images. The automation of detection from archival data proved to be complex, so issues related to the generation of a dense point cloud from this type of data were discussed in detail. The study revealed problems of archival images related to the poor identification of ground control points (GCP), insufficient overlap between images or poor radiometric quality of the scanned material. The research showed that over the 50 years, the built-up area increased as many as three times in the analysed area. The developed method of detecting buildings calculated at a level of more than 90% in the case of the LiDAR data and 88% based on the archival data.

Knowledge, Perceptions, and Practices on Risks and Disasters Among Medical Students. A Multicenter Cross-Sectional Study in 9 Latin American and Caribbean Countries.

The objective of this study was to describe the level of knowledge, perceptions, and practices in relation to risks and disasters in medical schools in Latin America and the Caribbean. Multicenter, observational, analytical, non-probabilistic convenience sample study with 2546 medical students in 9 countries of Latin America and the Caribbean. An online survey was conducted between October 2020 and November 2020, using an instrument validated in each country to assess knowledge, perceptions, and practices regarding risk and disaster prevention measures. Frequencies, percentages, mean and standard deviation (SD) were used for descriptive analysis. Differences resulting from the relationship between the variables studied and the level of knowledge were obtained using the Chi-square test. P-value <0.05 was accepted as statistically significant for all analyses. The highest proportion of responses came from women, third-semester students, and those studying in public universities. Students from Colombia and Honduras had the highest percentage of high levels of knowledge about disasters, while Peruvian students had the highest percentage of low levels of knowledge. Women and students from public universities showed a higher proportion of high levels of knowledge. 52.7% considered that they live in a country with a medium risk of natural disasters, while 91.2% said that Latin American and Caribbean countries are not prepared to face natural disasters. Only 43.6% believe they are prepared to help in the event of a natural disaster. Most of medical students from Latin America and Latin America and the Caribbean have high and medium level of knowledge in risks and disasters. However, the implementation of disaster training programs for medical students has the potential to improve the preparedness, knowledge, and skills that are important for medical personnel to improve their self-confidence, and their ability to respond, resulting in more effective systems.