Stochastic Risk Assessment Research Articles

Stochastic event-based probabilistic earthquake risk assessment framework for Uganda: towards informing the National Policy for Disaster preparedness and management

Abstract Catastrophic earthquakes in Uganda have the potential for detrimental consequences on the socio-economic welfare and resilience of communities. Despite considerable efforts in predicting earthquake risk across Africa, a national comprehensive seismic risk study for Uganda does not exist. With increasing population, urbanisation and rapid construction, seismic risk is escalating fast and is compounded by the high vulnerability of buildings and scanty disaster prevention and mitigation strategies. This study uses the probabilistic event-based risk calculator of the OpenQuake-engine to assess potential risks resulting from future earthquakes. Although the building exposure model is largely inferred and projected from the national population and housing census of 2014, total replacement costs are obtained by performing series of interviews with local engineering practitioners. Analytical vulnerability curves are selected from Global Earthquake Model (GEM) database. Seismic hazard studies confirm that western Uganda is exposed to the highest level of seismicity where peak ground accelerations on rock ground can reach up to 0.27 g over a 475-year return period. Relative to Uganda’s gross domestic product, the associated seismic risk estimates indicate mean economic loss ratios of 0.36%, 2.72% and 4.94% over 10, 50 and 100-year return periods respectively; with mean annual economic loss of US$ 74.7 million (0.34% relative to the total replacement value) and annual deaths averaging 71 persons across the whole country. It is envisaged that the findings will inform strategic land use planning patterns, earthquake insurance pricing and foster the continuous improvement of Uganda’s National Policy for Disaster Preparedness and Management.

Stochastic Risk Assessment Framework of Deep Shale Reservoirs by a Deep Learning Method and Random Field Theory

Risk assessment of deep shale reservoirs is very important for subsurface energy development. However, due to complex geological environments and physicochemical interactions, shale reservoir fabric parameters exhibit variability. Moreover, the actual investigation and testing information is very limited, which is a typical small-sample problem. In this paper, the heterogeneity and statistical characteristics of deep shale reservoirs are clarified by the measured mechanical parameters. A deep learning method for deep shale reservoirs with limited survey data information is proposed. The variability of deep shale reservoirs is characterized by random field theory. A variable stiffness method and stochastic analysis method are developed to evaluate the risk of deep shale reservoirs. The detailed workflow of the stochastic risk assessment framework is presented. The frequency distribution and failure risk of deep shale reservoirs are calculated and analyzed. The risk assessment of deep shale reservoirs under different model parameters is discussed. The results show that a stochastic risk assessment framework of deep shale reservoirs, using a deep learning method and random field theory, is scientifically reasonable. The scatter plots of the elasticity modulus (EM), cohesive force (CF), and Poisson ratio (PR) distribute along the 45-degree line. The different distributed variables of EM, CF, and PR have a positive correlation. The statistical properties of the measurement data and deep learning data are approximately the same. The principal stress of deep shale follows the normal distribution with significance level 0.1. Under positive copula conditions, the maximum failure probability is 5.99%. Under negative copula conditions, the maximum failure probability is 4.58%. Different copula functions under positive and negative copula conditions have different failure probabilities. For the exponential correlation structure, the minimum failure probability is 3.46%, while the maximum failure probability is 6.19%. The mean failure probability of the EM, CF, and PR of deep shale reservoirs is 4.85%. Different random field-related structures and parameters have different influences on the failure risk.

Contact pressure explains half of the abdominal aortic aneurysms wall thickness inter-study variability.

The stochastic rupture risk assessment of an abdominal aortic aneurysm (AAA) critically depends on sufficient data set size that would allow for the proper distribution estimate. However, in most published cases, the data sets comprise no more than 100 samples, which is deemed insufficient to describe the tails of AAA wall thickness distribution correctly. In this study, we propose a stochastic Bayesian model to merge thickness data from various groups. The thickness data adapted from the literature were supplemented by additional data from 81 patients. The wall thickness was measured at two different contact pressures for 34 cases, which allowed us to estimate the radial stiffness. Herein, the proposed stochastic model is formulated to predict the undeformed wall thickness. Furthermore, the model is able to handle data published solely as summary statistics. After accounting for the different contact pressures, the differences in the medians reported by individual groups decreased by 45%. Combined data can be fitted with a lognormal distribution with parameters μ = 0.85 and σ = 0.32 which can be further used in stochastic analyses.

Assessment of risk for aromatic hydrocarbons resulting from subsea Blowouts: A case study in eastern Canada

There is increasing concern over the environmental risks associated with deepwater petroleum exploration activities. The integration of environmental risk assessment and oil spill modeling can help to understand and quantitatively characterize the potential risks from subsea blowouts in specific regions. This study integrates a novel deepwater oil spill model (DWOSM) and an extended stochastic modeling methodology to assess the environmental risk of polycyclic aromatic hydrocarbons (PAHs) during a simulated offshore subsurface blowout off the east coast of Newfoundland, Canada. Additionally, the effectiveness of subsea dispersant injection (SSDI) in spill mitigation was investigated through comparative simulations. Resultant spill hazard and risk maps for current and proposed areas of offshore oil and gas development, in support of contingency plans, revealed that surfaced oil tends to drift toward the southeast and east in the hypothetical blowout case; nearshore areas of east Newfoundland have relatively low risk shortly after a deep-sea blowout; released PAHs may elicit more adverse ecological impacts than volatile organic compounds (VOCs); and SSDI application can reduce contaminant exposure levels but at the expense of enlarging the impacted zone for a short term. This stochastic simulation-based risk assessment provides scientific evidence to support decision-making in strategic oil spill response operations.

Analytical model for CO2-water displacement with rate-dependent phase permeability for geological storage

Analytical modelling is an effective tool for predicting reservoir behaviour under high uncertainties, like CO2 storage in aquifers, where multiple simulation runs are necessary for stochastic modelling and risk assessment. This paper formulates the Buckley-Leverett problem with x-dependent fractional flow. We derive a new analytical model for displacement of brine by CO2 accounting for (i) rate-dependent phase permeability during radial flows and (ii) radial flows of Forchheimer's high-rate gas injection; this analytical model is also valid for (iii) linear flows during flooding in micro heterogeneous or composite cores and (iv) CO2-water flow upscaling in reservoirs where layering is perpendicular to flux direction. An exact solution of the flow equation is based on the observation that the flux of each phase conserves along the characteristic trajectories. We discuss S-shape fractional flow function, which is typical for reservoir rocks. The solution includes formulae for phase saturations and fluxes and trajectories of the displacement CO2-water front and of the forward and rear mixture zone boundaries. The fast analytical model can be used for multivariate sensitivity study and sweep efficiency prediction.

Source-oriented stochastic health risk assessment of toxic metals in soil via a hybrid model and Monte Carlo simulation

Toxic metal contamination in soils poses significant hazards to the environment and human health; thus, quantitative assessment of the sources and risks of metal contaminants are urgently needed. A hybrid model that integrates the positive matrix factorization (PMF) and random forest (RF) methods was proposed to quantify the sources of toxic metals in soils by combining diverse environmental variables (source proxies) in this study. In addition, a health risk assessment and Monte Carlo simulations were integrated to estimate the source-oriented stochastic health risk. The results suggested that, except for Ni, which exhibited moderate contamination, other toxic metals (As, Cd, Cr, Hg and Pb) presented slight contamination. Four sources (agricultural activities loaded heavily by As, atmospheric deposition loaded heavily by Hg and Pb, natural sources and mining activities loaded heavily by Cr and Ni, and industrial activities loaded heavily by Cd) were defined and explained 23.44 %, 26.65 %, 30.13 % and 19.78 % of the total variance in toxic metals, respectively. The principal route of exposure (i.e., ingestion), the population at highest risk (i.e., children), and the most hazard-inducing metals (i.e., As and Cr) were determined. Agricultural activities and the combination of natural sources and mining activities demonstrated certain degrees of noncarcinogenic risk to children, with exceedance ratios of 2.20 % and 2.56 %, respectively. Additionally, the combination of natural sources and mining activities demonstrated probabilities for significant carcinogenic risk to adults and children of 0.59 % and 3.76 %, respectively. To reduce the health risks of toxic metals in soils and to protect food and ecological safety, strict regulations should be established to control the discharge of waste from mining and agricultural activities.

Stochastic health risk assessment of aflatoxin M1 in cow's milk among Lebanese population

This study quantitatively assessed AFM1 exposure through consumption of cows' milk in Lebanese adolescents and adults. Lebanon-specific, non-aggregated data on (i) milk intake and body weight – from an existing survey for adults and from a new survey for adolescents, and (ii) AFM1 occurrence in milk, were fitted to distributions and incorporated into a probabilistic model. Risk of hepatocellular carcinoma (HCC) for milk consumers was estimated considering the incidence of hepatitis B virus in Lebanon and characterized using the margin of exposure (MOE). All subgroups’ mean risk estimates, expressed as additional HCC cases per 100 000 population per year, were significantly different (p < 0.05), and ranged from 2.2x10−4 for adult males to 4.9x10−4 for adolescent males. This variation is attributed to significant differences in body weight and milk intake. MOE identified adolescent males and females as at-risk populations (mean 9628 and 8108, respectively). For adults, levels of concern were only reached with extreme consumption and/or contamination events. Considering that exposure from other sources (e.g., dairy) is cumulative, control of AFM1 in milk in Lebanon should be a risk management priority to ensure protection of younger populations.

Integrating meta-analysis with a quantitative microbial risk assessment model to investigate Campylobacter contamination of broiler carcasses

This study investigated the prevalence and associated risk factors of Campylobacter in South Korean broilers using a random-effects meta-analysis. Subsequently, to facilitate the design of preventive measures, the prevalence estimate from the meta-analysis was incorporated into a stochastic risk assessment model to quantify the Campylobacter contamination levels on broiler carcasses. The baseline model was developed based on the most common practices along the South Korean broiler processing line, with no interventions. Meta-analysis results revealed Campylobacter prevalence across the chicken supply chain in the following order: farms (60.6 % [57.3–63.4]), retail markets (43.90 % [24.81–64.99]), slaughterhouses (27.71 % [18.56–39.21]), and processing plants (14.50 % [3.96–41.09]). The model estimated a 52 % (36.1–70.8) Campylobacter prevalence at the end of chilling, with an average contamination level of 4.62 (2.50–6.74) log CFU/carcass. Sensitivity analysis indicated that Campylobacter fecal shedding (r = 0.95) and the amount of feces on bird exteriors (r = 0.17) at pre-harvest were the main factors for carcass contamination, while soft scalding (r = -0.22) and air chilling (r = -0.12) can serve as critical control points (CCPs) at harvest. Scenario analysis indicated that a combination of hard scalding, inside-outside bird washing, spray washing, and chlorinated water immersion chilling can offer a 30.9 % reduction in prevalence and a reduction of 2.23 log CFU/carcass in contamination levels compared to the baseline model. Apart from disinfection and sanitation interventions carried out during meat processing, the implementation of robust control measures is indispensable to mitigate Campylobacter prevalence and concentration at broiler farms, thereby enhancing meat safety and public health. Furthermore, given the high Campylobacter prevalence in the retail markets, future studies should explore the potential risk of cross-contamination at post-harvest stage.

Malcoda: Practical and Stochastic Security Risk Assessment for Enterprise Networks

Malcoda: Practical and Stochastic Security Risk Assessment for Enterprise Networks

Stochastic Precipitation Model Using Large Ensemble Data

A precipitation dataset is created to estimate a reproduction period of several thousand years for stochastic flood risk assessment in the non-life insurance sector. A stochastic precipitation model for natural hazard risk assessment developed in a previous study was applied to a large ensemble data. The model was used to obtain the precipitation ensembles for the recent and future climate by +2 K and +4 K increases in mean temperature, respectively. We successfully created 10,000 years of precipitation data, which makes it possible to obtain precipitation data over a 1,000-year return period.

Laboratory testing and on-site storage are successful at mitigating the risk of release of foot-and-mouth disease virus via production of bull semen in the USA.

Thousands of frozen bovine semen doses are produced daily in the US for domestic use. An incursion of foot-and-mouth disease (FMD) in the country would pose strong challenges to the movements of animals and animal products between premises. Secure supply plans require an estimation of the risk associated with target commodities and the effectiveness of mitigation measures. This study presents the results of a quantitative assessment of the risk of release of FMD virus from five of the largest commercial bull studs in the US via contaminated frozen processed semen. The methodology from a previous study was adapted to better fit the US production system and includes more recent data. Two models were combined, a deterministic compartmental model of FMD transmission and a stochastic risk assessment model. The compartmental model simulated an FMD outbreak within a collection facility, following the introduction of a latent-infected bull. The risk of release was defined as the annual likelihood of releasing at least one frozen semen batch, defined as the total amount of semen collected from a single bull on a given collection day, containing viable FMD virus. A scenario tree was built using nine steps leading from the collection to the release of a contaminated batch from a given facility. The first step, the annual probability of an FMD outbreak in a given facility, was modeled using an empirical distribution fitted to incidence data predicted by five models published between 2012 and 2022. An extra step was added to the previously published risk pathway, to account for routine serological or virological surveillance within facilities. The results showed that the mitigation measures included in the assessment were effective at reducing the risk of release. The median annual risk of release from the five facilities was estimated at less than 2 in 10 billion (1.5 x 10-10) in the scenario including a 30-day storage, routine genome detection assays performed every two weeks and RT-PCR testing of the semen. In this scenario, there was a 95% chance that the risk of release would be lower than 0.00041. This work provides strong support to the industry for improving their response plans to an incursion of FMD virus in the US.

Training in tools to develop quantitative microbial risk assessment of ready-to-eat food with a comparison between the Romanian and Spanish food supply chains.

The prevention and control of bacterial contamination on ready-to-eat (RTE) fresh produce is an essential task to ensure food safety. Therefore, the development of novel and effective decontamination technologies to ensure microbiological safety of fruits and vegetables has gained considerable attention and new sanitisation methods are needed. The antimicrobial activity of essential oils (EOs) is well documented, but their application in fresh produce remains a challenge due to their hydrophobic nature. Thus, nanoemulsions efficiently contribute to support the use of EOs in foods by enhancing their dispersibility, their contact area and facilitating the introduction into bacterial cells. The combination of these factors ultimately increases their antimicrobial activity. Quantitative microbial risk assessment (QMRA) is gaining more attention as an effective tool to assess and prevent potential risks associated with food-borne pathogens. In this context, the current project aims to study the effectiveness of different washing methods based on nanoemulsified EOs, comparing them against traditional methods, using a QMRA model for Escherichia coli O157:H7 on cherry tomatoes. Different simulations within a stochastic risk assessment model were implemented using the biorisk package for R, aiming to describe microbial behaviour and biological risk along the Romanian and Spanish food supply chains of RTE fresh produce. Nanoemulsions were prepared using oregano and rosemary EOs, each from Romania and Spain. The four nanoemulsions were evaluated as decontamination treatments to control the growth of E. coli O157:H7 on artificially contaminated cherry tomatoes. The decontamination treatments showed encouraging results, comparable to commonly used chlorine solutions. Therefore, oregano and rosemary nanoemulsions are promising and could be a feasible alternative for chlorine solutions in the reduction of microbiological contaminants.

Quantitative risk assessment of African swine fever introduction into Spain by legal import of swine products

African swine fever (ASF) is currently threatening the global swine industry. Its unstoppable global spread poses a serious risk to Spain, one of the world's leading producers. Over the past years, there has been an increased global burden of ASF not only in swine but also swine products. Unfortunately, many pigs are not diagnosed before slaughter and their products are used for human consumption. These ASF-contaminated products are only a source for new ASF outbreaks when they are consumed by domestic pigs or wild boar, which may happen either by swill feeding or landfill access. This study presents a quantitative stochastic risk assessment model for the introduction of ASF into Spain via the legal import of swine products, specifically pork and pork products. Entry assessment, exposure assessment, consequence assessment and risk estimation were carried out. The results suggest an annual probability of ASF introduction into Spain of 1.74 × 10−4, the highest risk being represented by Hungary, Portugal, and Poland. Monthly risk distribution is homogeneously distributed throughout the year. Illegal trade and pork product movement for own consumption (e.g., air and ship passenger luggage) have not been taken into account due to the lack of available, accredited data sources. This limitation may have influenced the model's outcomes and, the risk of introduction might be higher than that estimated. Nevertheless, the results presented herein would contribute to allocating resources to areas at higher risk, improving prevention and control strategies and, ultimately, would help reduce the risk of ASF introduction into Spain.

Release of live baitfish by recreational anglers drives fish pathogen introduction risk.

Emerging diseases of wildlife are an existential threat to biodiversity, and human-mediated movements of live animals are a primary vector of their spread. Wildlife disease risk analyses offer an appealing alternative to precautionary approaches because they allow for explicit quantification of uncertainties and consideration of tradeoffs. Such considerations become particularly important in high-frequency invasion pathways with hundreds of thousands of individual vectors, where even low pathogen prevalence can lead to substantial risk. The purpose of this study was to examine the landscape-level dynamics of human behavior-mediated pathogen introduction risk in the context of a high-frequency invasion pathway. One such pathway is the use and release of live fish used as bait by recreational anglers. We used a stochastic risk assessment model parameterized by angler survey data from Minnesota, USA, to simulate one year of fishing in Minnesota and estimate the total number of risky trips for each of three pathogens: viral hemorrhagic septicemia virus, the microsporidian parasite Ovipleistophora ovariae, and the Asian fish tapeworm Schizocotyle acheilognathi. We assessed the number of introductionsunder four scenarios: current/baseline conditions, outbreak conditions (increased pathogen prevalence), source-focused control measures (decreased pathogen prevalence), and angler-focused control measures (decreased rates of release). We found that hundreds of thousands of introduction events can occur per year, even for regulated pathogens at low pathogen prevalence. Reducing the rate of illegal baitfish release had significant impact on risky trips in scenarios where a high number of anglers were involved, but was less impactful in circumstances with limited outbreaks and fewer affected anglers. In contrast, reducing pathogen prevalence in the source populations of baitfish had relatively little impact. In order to make meaningful changes in pathogen introduction risk, managers should focus efforts on containing local outbreaks and reducing illegal baitfish release to reduce pathogen introduction risk. Our study also demonstrates the risk associated with high-frequency invasion pathways and the importance of incorporating human behaviors into wildlife disease models and risk assessments.

Quantitative microbiological risk assessment model for Campylobacter in raw milk of dairy cows in Germany

The consumer demand for raw milk from dairy cows has increased and local sales via vending machines have been intensified. Therefore, this study aimed to assess the risk associated with the consumption of unboiled raw milk contaminated with Campylobacter by estimating the number of campylobacteriosis cases. For this a stochastic quantitative microbial risk assessment (QMRA) model was developed that covered the whole supply chain. Information and data for model parametrization were obtained from research publications. Different probability distributions were used to represent the data whenever possible and probabilistic risk estimation was performed using Monte Carlo simulations. Simulations for outbreaks from single vending machines were performed using the developed QMRA baseline model. Further, different risk mitigation scenarios along the supply chain were evaluated to support risk managers in controlling Campylobacter. The analysis suggest a role for Campylobacter infections due to fecal contamination of cows’ udder. The model can easily be adapted and extended when additional data become available as it is provides in the harmonized exchange Food Safety Knowledge Exchange (FSKX) format.

Stochastic Risk Assessment with Bayesian Networks in Esfahan Refinery

Refineries are among the industrial centers that supply the energy and raw materials to downstream industries. To achieve sustainable development goals, creating appropriate balance between economic and environmental goals has always been the focus of managers and policy makers in the societies. Bayesian Network model has become a robust tool in the field of risk assessment and uncertainty management in refineries. The focus of this research is to prioritizing different units from the point of view of social and ecological aspects for facilitating the decision-making process in the context of waste material treatment in Esfahan refinery in line with the sustainable development goals. The methodology of this research is based on risk assessment with the aid of Bayesian Networks. To this end, first material flow analysis of the processes procured risk identification, subsequently influence diagram and Bayesian Network structure were designed. After completing conditional probability tables, risk factors were prioritized. According to the risk assessment results, Fuel unit was classified as the most significant risk factor, whereas Pipelines and Plant air &amp; instrument air system were identified as the most environmentally friendly units.

Comparative analysis and human health risk assessment of contamination with heavy metals of Central Asian rivers

The study focuses on heavy metals contamination, drinking water quality, and associated health risks for adults and children by consuming water from Central Asian Rivers (Syr-Darya, Nura, and Ili Rivers). Water samples were collected from three rivers within the 2014–2019 period by the RMS “Kazhydromet” and analyzed for various physicochemical parameters. The study revealed that the concentrations of Fe, Cd, Cr (VI), Hg, Mn, and As significantly exceeded local and international drinking water standards in at least one water body. The lowest total water quality index (55.1%) was observed in the Nura River (“marginal water category”). Coal, soil, non-ferrous metals, and iron ore industries were found to be the major sources of heavy metals in the regions. Deterministic risk assessment revealed serious cancer risks (>1E−5) in rivers due to As and Cr (VI) exposure by oral and dermal contact for adults and children. Stochastic risk assessment confirmed high cancer risks (>1E−4) due to Cr (VI) contamination of the Syr-Darya River. The study results indicate the serious lifetime cancer risk to the residents due to the use of river water for drinking and household activities. Therefore, the study area urgently and continuously requires heavy metal removal, effective monitoring, and good quality drinking water supply.

Fuzzy radon hazard index assessment for stochastic environmental health risk evaluation of urban scale building

Radon gas emission is emerging phenomenon that poses potential danger to human health as a result of modern lifestyles. Thus, it is critical to conduct stochastic evaluations of the amount of this hazardous gas in urban areas and residential buildings to identify environmental health risks. To assess statistical radon environmental health risks, this research proposes two novel fuzzy radon hazard indices (FRHIs), FRHI1 and FRHI2.. FRHI1 can contribute to every standard, while FRHI2 can be compared to geogenic radon potential or geogenic radon hazard indices. The output indices FRHIs range from 0 (no hazard) to 100 (the highest degree of hazard). The proposed approach can serve as a circumstantially integrated standard for stochastic radon risk assessment and management, so that fuzzification can bring innovation in stochastic standards in this field. In this study, radon concentration was measured in an urban building and natural radon and emission zoning maps were created using ArcGIS software at urban and geological scales. A residential building unit located in a critical area was selected and some corrective actions were adopted to reduce radon in urban building units. The FRHI Assessment for stochastic environmental health risk evaluation showed that the initial fuzzy level for the mean value of FRHI is hazardous (for an FRHI value equal to 60.1), indicated by red. However, the Maximum FRHI level for 48 h after the installation was rather hazardous (for an FRHI value equal to 44.8), indicated by orange. After steady-state installation, the maximum statistical environmental health risk would fall into an improved category. Identifying critical areas can provide exceptional control at the urban scale building that reduces the risks of natural radon.

A Stochastic Approach to Determine the Optimal Number of Servers for Reliable and Energy Efficient Operation of Data Centers

The increasing demand of the data center's computational capacity in recent years has introduced new data center operational challenges among others to maintain the service level agreements (SLA) and quality of services (QoS), while at the same time limiting energy consumption. In this paper, a stochastic operational risk assessment approach is presented that estimates the required number of spare servers in a data center considering the risk of servers' failure in operation since servers define the computational capability of a data center. A reliability index called “risk of computational resource commitment (RCRC)” is introduced that quantifies the probability of having insufficient spare servers due to failures during the operational lead time, and the complement of the RCRC shows the ability of the resources to maintain SLA of a data center. The failure rates of the servers are obtained using a Monte Carlo Simulation with the failure data, published by Google in 2019. The analysis shows that the RCRC reduces with the increasing number of spare servers, while it also stresses the energy efficiency of the data center. The RCRC index could be used in data center operation to avoid overprovisioning of the servers and to limit the number of spare servers in the data center, while creating a suitable balance between QoS and energy consumption of the data centers.

Climate change effects on loss assessment and mitigation of residential buildings due to hurricane wind

Hurricanes are considered as one of the most devastating natural hazards. Their induced risk is expected to significantly increase with the changing climate conditions. Residential buildings are specifically highly vulnerable to hurricane winds. Therefore, accurate assessment of hurricane induced losses under changing climate conditions is crucial to assist in the development of the best mitigation strategies. In this paper, a stochastic hurricane risk assessment framework accounting for several climate scenarios is developed to assess the hurricane-induced losses of residential buildings located in hurricane-prone regions. Specifically, a total of 10,000 years of synthetic storms will be generated for both ‘observed climate’ and ‘future climate’. The future climate models correspond to the worst-case scenario SSP5-8.5 and are simulated based on two global climate models. Then a physics-based wind model is coupled with the synthetic tracks to generate the hazard probabilities. To accurately capture the upper tail effects, the Gaussian kernel density estimation function is proposed in this study. The effects of the variability of climate change models on the wind hazard and its induced losses are quantitatively evaluated. The average annual regional loss is selected as the decision variable. The framework is applied to a one-story single-family wood frame residential building in three different cities, namely Atlantic City, Miami and Galveston. Three mitigation strategies are evaluated, with varying degrees of structural mitigation, by comparing the corresponding losses incurred under different climate change scenarios. It is shown that, due to the disproportionate variation of hurricane intensity and frequency over the coastal areas, the estimated annual losses with the consideration of several mitigation strategies change unevenly from one location to another. Also, the selection of inappropriate probability density function to estimate the wind distribution might underestimate the hurricane-induced loss which is mainly due to its incapability to accurately capture the upper tail ends of the wind distribution.