Insuring Well-being: Psychological Adaptation to Disasters

We examine the impact of life and health insurance spending on subjective well-being. Taking advantage of insurance spending and subjective well-being data on more than 700,000 individuals in Japan, we examine whether insurance spending can buffer declines in subjective well-being due to exposure to mass disaster. We find that insurance spending can buffer drops in subjective well-being by approximately 3–6% among those who experienced the mass disaster of the great East Japan earthquake. Subjective health increases the most, followed by life satisfaction and happiness. On the other hand, insurance spending decreases the subjective well-being of those who did not experience the earthquake by approximately 3–7%. We conclude by monetizing the subjective well-being loss and calculating the extent to which insurance spending can compensate for it. The monetary value of subjective well-being buffered through insurance spending is approximately 33,128 USD for happiness, 33,287 USD for life satisfaction, and 19,597 USD for subjective health for a person in one year. Therefore, we confirm that life/health insurance serves as an ideal option for disaster adaptation. Our findings indicate the importance of considering subjective well-being, which is often neglected when assessing disaster losses.

Bayesian Predictive Analysis of Natural Disaster Losses

Different types of natural events hit the United States every year. The data of natural hazards from 1900 to 2016 in the US shows that there is an increasing trend in annul natural disaster losses after 1980. Climate change is recognized as one of the factors causing this trend, and predictive analysis of natural losses becomes important in loss prediction and risk prevention as this trend continues. In this paper, we convert natural disaster losses to the year 2016 dollars using yearly average Consumers Price Index (CPI), and conduct several tests to verify that the CPI adjusted amounts of loss from individual natural disasters are independent and identically distributed. Based on these test results, we use various model selection quantities to find the best model for the natural loss severity among three composite distributions, namely Exponential-Pareto, Inverse Gamma-Pareto, and Lognormal-Pareto. These composite distributions model piecewise small losses with high frequency and large losses with low frequency. Remarkably, we make the first attempt to derive analytical Bayesian estimate of the Lognormal-Pareto distribution based on the selected priors, and show that the Lognormal-Pareto distribution outperforms the other two composite distributions in modeling natural disaster losses. Important risk measures for natural disasters are thereafter derived and discussed.

Natural and Socioeconomic Factors and Their Interactive Effects on House Collapse Caused by Typhoon Mangkhut

Typhoons are an environmental threat that mainly affects coastal regions worldwide. The interactive effects of natural and socioeconomic factors on the losses caused by typhoon disasters need further examination. In this study, GeoDetector was used to quantify the determinant powers of natural and socioeconomic factors and their interactive effects on the rate of house collapse in Guangdong and Guangxi Provinces of southeast China caused by Typhoon Mangkhut in 2018. We further identify the dominant factors that influenced the disaster losses. The local indicators of spatial association method was then introduced to explain the spatial heterogeneity of the disaster losses under the influence of the dominant factor. The results indicate that both natural and socioeconomic factors significantly affected the house collapse rate. The maximum precipitation was the dominant factor, with a q value of 0.21, followed by slope and elevation, with q values of 0.17 and 0.13, respectively. Population density and per capita gross domestic product had q values of 0.15 and 0.13, respectively. Among all of the interactive effects of the influencing factors, the interactive effect of elevation and the ratio of brick-wood houses had the greatest influence (q = 0.63) on the house collapse rate. These results can contribute to the formulation of more specific safety and property protection policies.

Vulnerability Analysis to Drought Based on Remote Sensing Indexes

A vulnerability curve is an important tool for the rapid assessment of drought losses, and it can provide a scientific basis for drought risk prevention and post-disaster relief. Those populations with difficulty in accessing drinking water because of drought (hereon “drought at risk populations”, abbreviated as DRP) were selected as the target of the analysis, which examined factors contributing to their risk status. Here, after the standardization of disaster data from the middle and lower reaches of the Yangtze River in 2013, the parameter estimation method was used to determine the probability distribution of drought perturbations data. The results showed that, at the significant level of α = 0.05, the DRP followed the Weibull distribution, whose parameters were optimal. According to the statistical characteristics of the probability density function and cumulative distribution function, the bulk of the standardized DRP is concentrated in the range of 0 to 0.2, with a cumulative probability of about 75%, of which 17% is the cumulative probability from 0.2 to 0.4, and that greater than 0.4 amounts to only 8%. From the perspective of the vulnerability curve, when the variance ratio of the normalized vegetation index (NDVI) is between 0.65 and 0.85, the DRP will increase at a faster rate; when it is greater than 0.85, the growth rate of DRP will be relatively slow, and the disaster losses will stabilize. When the variance ratio of the enhanced vegetation index (EVI) is between 0.5 and 0.85, the growth rate of DRP accelerates, but when it is greater than 0.85, the disaster losses tend to stabilize. By comparing the coefficient of determination (R2) values fitted for the vulnerability curve, in the same situation, EVI is more suitable to indicate drought vulnerability than NDVI for estimating the DRP.

Measuring Community Disaster Resilience in the Conterminous Coastal United States

In recent years, building resilient communities to disasters has become one of the core objectives in the field of disaster management globally. Despite being frequently targeted and severely impacted by disasters, the geographical extent in studying disaster resilience of the coastal communities of the United States (US) has been limited. In this study, we developed a composite community disaster resilience index (CCDRI) for the coastal communities of the conterminous US that considers different dimensions of disaster resilience. The resilience variables used to construct the CCDRI were justified by examining their influence on disaster losses using ordinary least squares (OLS) and geographically weighted regression (GWR) models. Results suggest that the CCDRI score ranges from −12.73 (least resilient) to 8.69 (most resilient), and northeastern communities are comparatively more resilient than southeastern communities in the study area. Additionally, resilience components used in this study have statistically significant impact on minimizing disaster losses. The GWR model performs much better in explaining the variances while regressing the disaster property damage against the resilience components (explains 72% variance) than the OLS (explains 32% variance) suggesting that spatial variations of resilience components should be accounted for an effective disaster management program. Moreover, findings from this study could provide local emergency managers and decision-makers with unique insights for enhancing overall community resilience to disasters and minimizing disaster impacts in the study area.

Role of IoT devices in Flood Monitoring System using Social Networking Sites

Each country has a natural disaster, but catastrophe losses can't be avoided. The loss of human life, damage to the environment, infrastructure degradation, etc. Which in turn affects the country's development facing the disaster's wrath? In this analysis, we discuss the various methods available in the literature to reduce the losses in flood-related natural disasters. There are four major steps in the prevention of disaster losses, including preparedness, response, recovery and mitigation. Existing methods that address the above steps and all the current methods have certain limitations and are therefore not all sufficient to minimize losses due to flooding. In order to overcome all the deficiencies in the exit method, we propose an IoT devices based algorithm to get the number of victims and survivors due to flood and reduce the flood losses model using social networking sites.