Natural Disaster Risk Research Articles

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.

Performance Evaluation and Improvement of CMFD's Precipitation Products Over Shanghai City, China

AbstractPrecipitation products with high resolution for urban areas remain an important and challenging issue. The main purposes of this study are to evaluate the accuracy of the China Meteorological Forcing Data Set (CMFD) in the Shanghai megacity and to improve the accuracy of the CMFD precipitation. With the rain gauge observations for the 2013–2018 period, we evaluate the error and detection ability of CMFD at the sub‐daily, daily, and monthly scales, and examine the spatial variation of CMFD performance over the region. The Geographic Differential Analysis (GDA) method is used to calibrate the CMFD precipitation data set. The results show that the performance of CMFD precipitation varies from sub‐daily to monthly time scales with better accuracy at the monthly scale in the study region. The CMFD can capture the spatial variability over the region with less error in the north and outliers in the center. The GDA‐based calibration framework can significantly improve the capability of CMFD products, especially at the sub‐daily and daily scales. The calibrated CMFD can provide reliable precipitation estimates and capture more extreme precipitation events. The improved CMFD products can provide a key basis for applications related to hydrological and meteorological hazard monitoring and assessment in the Shanghai area. The overall framework is scalable and could be readily generalized to other regions, and the calibrated product can be used in the research of urban natural disaster risks and other studies.

How should ports share risk of natural and climate change disasters? Analytical modelling and implications for adaptation investments

This study theoretically examines disaster adaptation investments under risk of natural disasters. Given two neighboring, competitive ports, the disasters may cause independent damages to either port, or to both ports simultaneously; consequently, some shippers avoid loss by using the unaffected port if an independent disaster occurs in their local port. Since such inter-port risk sharing benefit increases with the share of independent disasters in all disasters, the socially optimal investment decreases with the disaster independence. However, the risk sharing benefit only attributes to the shippers' surplus and does not attribute to profits from the port management, so it does not affect investment of private port authorities that maximize the profits. Such an ignorance of the risk sharing benefit by the private port authorities is likely to lead to underinvestment in disaster adaptation facilities under a lower disaster independence.

SBAS-InSAR based validated landslide susceptibility mapping along the Karakoram Highway: a case study of Gilgit-Baltistan, Pakistan

Geological settings of the Karakoram Highway (KKH) increase the risk of natural disasters, threatening its regular operations. Predicting landslides along the KKH is challenging due to limitations in techniques, a challenging environment, and data availability issues. This study uses machine learning (ML) models and a landslide inventory to evaluate the relationship between landslide events and their causative factors. For this, Extreme Gradient Boosting (XGBoost), Random Forest (RF), Artificial Neural Network (ANN), Naive Bayes (NB), and K Nearest Neighbor (KNN) models were used. A total of 303 landslide points were used to create an inventory, with 70% for training and 30% for testing. Susceptibility mapping used Fourteen landslide causative factors. The area under the curve (AUC) of a receiver operating characteristic (ROC) is employed to compare the accuracy of the models. The deformation of generated models in susceptible regions was evaluated using SBAS-InSAR (Small-Baseline subset-Interferometric Synthetic Aperture Radar) technique. The sensitive regions of the models showed elevated line-of-sight (LOS) deformation velocity. The XGBoost technique produces a superior Landslide Susceptibility map (LSM) for the region with the integration of SBAS-InSAR findings. This improved LSM offers predictive modeling for disaster mitigation and gives a theoretical direction for the regular management of KKH.

Competencies for designing resilient systems in engineering education – a content analysis of selected study programs of five European technical universities

ABSTRACT Climate change increases the risk of natural disasters, which requires the design of sustainable and resilient infrastructure. This implies a need to enable engineering students to deal with highly complex problems and uncertainty. These competencies refer to the concept of resilience, describing the ability of a system to adapt to threatening events and to learn from failure. In this study, we investigated if and if so, how far resilience-related competencies are addressed within engineering study programs. Based on a content analysis, the learning outcomes from 48 study programs of five leading technical universities in Europe were analysed. The results indicate a lack of both resilience-related competencies within engineering programs and a consistent implementation of learning outcomes within the European Higher Education Area. Accordingly, future engineering professionals need to be more extensively educated to design resilient systems and higher education institutions need to purposefully embed these abilities in their study programs.

The Role of Corporate Social Responsibility (CSR) on Natural Disaster Risk Reduction in Indonesia

Indonesia has geographic, demographic, and sociological conditions that cause Indonesia to have a relatively high percentage and risk of natural disasters. The Ministry of Finance noted that every year losses due to natural disasters reach Rp. 20 trillion. In addition, the impact of natural disasters on society is also negatively affected by economic conditions. This condition creates a real urgency for the role of all levels of society in making social contracts to help each other during emergencies. The Corporate Social Responsibility (CSR) program is a form of corporate responsibility in paying attention to the welfare of the community as a form of corporate social and economic responsibility in carrying out its operations. Currently, the awareness of business entities in preventing natural disasters is still not optimal and the role of CSR is still limited to charity. This journal will review the role of companies through Corporate Social Responsibility (CRR) in reducing the risk of natural disasters in Indonesia.

Spatial distributions in disaster risk vulnerability for people with disabilities in the U.S.

BackgroundThe spread of Coronavirus Disease 2019 (COVID-19) in the United States has centered the role of natural hazards such as pandemics into the public health sphere. The impacts of these hazards disproportionately affect people with disabilities, who are frequently in situations of social, political, or economic disadvantage. Because of these disadvantages, people with disabilities may have less access to necessary resources and services, putting them at risk due to unmet health needs. These disparities in access also highlight important regional, state, and county-level differences with regards to vulnerability and preparedness for natural hazards. ObjectiveThe objective of this paper is to examine the relationship between disability and disaster risk in the United States. We examine the geographic variation in the relationship between risk from natural disasters and the percentage of people with disabilities living in a community. Because emergency management functions in the U.S. are directed and enacted at the county level, we also explore how these relationships change across U.S. counties. In addition to the overall prevalence of people with disabilities, we disaggregate the population of people with disabilities by gender, race, ethnicity, age, and disability impairment type. MethodsTo measure risk of natural hazards, we use Expected Annual Loss index, a component of the 2020 National Risk Index, developed by Federal Emergency Management Agency, which identifies communities most at risk to18 natural hazards. We measure the percent of people with disabilities per county using the American Community Survey. We estimate the nationwide relationship between the proportion of people with disabilities and risk of natural hazards using ordinary least squares regression. To explore geographic differences in these relationships across the United States, we use a geographically weighted regression model to estimate local relationships for each county in the contiguous United States. We use mapping techniques to display regional differences across different disability demographic groups. ResultsCounties with higher percentages of people with disabilities have a lower risk of natural disasters. Across the United States, a one percent increase in prevalence of people with disabilities in a county is associated with two percent decrease in the natural hazard risk score. Small but statistically significant regional differences exist as well. County-specific estimates range from a five percent decrease to a one percent increase. Stronger associations between risk and the prevalence of people with disabilities are observed in the Midwest and parts of the Southwest and West, whereas the relationship across racial groups is more scattered across the United States. ConclusionIn this study, nationwide results suggest that people with disabilities are more likely to live in communities with lower risk of natural hazards, but this relationship differs across U.S. counties and by demographic subgroups. These findings represent a contribution in further understanding the health and well-being of people with disabilities in the United States and the geographic variation therein.

The Coping Strategies Patterns Based on Local Wisdom and Resilience Capital in Facing Natural Disaster Risk in Nagari Mandeh, Indonesia

The Coping Strategies Patterns Based on Local Wisdom and Resilience Capital in Facing Natural Disaster Risk in Nagari Mandeh, Indonesia

Failure analysis of monolithic precast shear wall structure subjected to strong earthquake

In recent years, the risks of natural (earthquakes) and artificial (terrorist attacks) disasters to buildings and communities have attracted increasing attention. On the other hand, an increasing number of prefabricated building structures have been constructed in urban areas of China owing to the promotion of the government. The failure behaviours of prefabricated building structures subjected to disastrous actions have not been thoroughly investigated and understood. Therefore, in this study, we have established a finite element model of the monolithic prefabricated structure. A damage plasticity model was introduced to describe the failure of concrete materials and critical members of the structure. Using the failure criterion and element deletion techniques, the overall failure of the structure in the pattern of total collapse was obtained. The analytical results showed that effective vertical connections can improve the collapse resistance of prefabricated structures. The simulation results verified the proposed method and provided new insights for the anti-collapse design of monolithic precast structures.

Quantitative Study on Agricultural Premium Rate and Its Distribution in China

In recent years, with the deepening of the reform of rural economic systems, the demand for disaster risk governance in land production and management is increasing, and it is urgent for the state to develop agricultural insurance to improve land production recovery capacity and ensure national food security. The study develops a quantitative model to determine the agricultural premium rate for each county in China based on disaster risk level in order to refine agricultural insurance. The results show that: (a) in terms of the disaster situation, most of northeast and central China, part of southwest, north, and northwest China are seriously affected; (b) regarding the integrated natural disaster risk level, there are 129 counties with extremely high disaster risk in China; (c) as for agricultural premium rates based on the integrated natural disaster risk index, some counties in Inner Mongolia, Shanxi, Liaoning, Jilin, Shandong, Anhui, Jiangxi, Zhejiang, Guangdong, Hubei, and Hunan Province had extremely high rates, out of a total of 63 counties. The above results reveal regional differences in disaster risk levels and premium rates between counties, providing a reference for improving the accuracy of agricultural premium rates. This contributes to the creation of security for further improving land production capacity and promoting the intensification and sustainable development of agricultural production.