Probabilistic Modeling of Flood Hazard and its Risk Assessment for Eastern Region of India

This article models flood occurrence probabilistically and its risk assessment. It incorporates atmospheric parameters to forecast rainfall in an area. This measure of precipitation, together with river and ground parameters, serve as parameters in the model to predict runoff and subsequently inundation depth of an area. The inundation depth acts as a guide for predicting flood proneness and associated hazard. The vulnerability owing to flood has been analyzed as social vulnerability $(V_S)$, vulnerability to property $(V_P)$, and vulnerability to the location in terms of awareness $(V_A)$. The associated risk has been estimated for each area. The distribution of risk values can be used to classify every area into one of the six risk zones—namely, very low risk, low risk, moderately low risk, medium risk, high risk, and very high risk. The prioritization regarding preparedness, evacuation planning, or distribution of relief items should be guided by the range on the risk scale within which the area under study falls. The flood risk assessment model framework has been tested on a real‐life case study. The flood risk indices for each of the municipalities in the area under study have been calculated. The risk indices and hence the flood risk zone under which a municipality is expected to lie would alter every day. The appropriate authorities can then plan ahead in terms of preparedness to combat the impending flood situation in the most critical and vulnerable areas.     

A Robust Approach for Mitigating Risks in Cyber Supply Chains

In recent years, there have been growing concerns regarding risks in federal information technology (IT) supply chains in the United States that protect cyber infrastructure. A critical need faced by decisionmakers is to prioritize investment in security mitigations to maximally reduce risks in IT supply chains. We extend existing stochastic expected budgeted maximum multiple coverage models that identify “good” solutions on average that may be unacceptable in certain circumstances. We propose three alternative models that consider different robustness methods that hedge against worst‐case risks, including models that maximize the worst‐case coverage, minimize the worst‐case regret, and maximize the average coverage in the $(1?\alpha )$ worst cases (conditional value at risk). We illustrate the solutions to the robust methods with a case study and discuss the insights their solutions provide into mitigation selection compared to an expected‐value maximizer. Our study provides valuable tools and insights for decisionmakers with different risk attitudes to manage cybersecurity risks under uncertainty.     

Logistic Regression Models with Unspecified Low Dose–Response Relationships and Experimental Designs for Hormesis Studies

Hormesis refers to a nonmonotonic (biphasic) dose–response relationship in toxicology, environmental science, and related fields. In the presence of hormesis, a low dose of a toxic agent may have a lower risk than the risk at the control dose, and the risk may increase at high doses. When the sample size is small due to practical, logistic, and ethical considerations, a parametric model may provide an efficient approach to hypothesis testing at the cost of adopting a strong assumption, which is not guaranteed to be true. In this article, we first consider alternative parameterizations based on the traditional three‐parameter logistic regression. The new parameterizations attempt to provide robustness to model misspecification by allowing an unspecified dose–response relationship between the control dose and the first nonzero experimental dose. We then consider experimental designs including the uniform design (the same sample size per dose group) and the $c$‐optimal design (minimizing the standard error of an estimator for a parameter of interest). Our simulation studies showed that (1) the $c$‐optimal design under the traditional three‐parameter logistic regression does not help reducing an inflated Type I error rate due to model misspecification, (2) it is helpful under the new parameterization with three parameters (Type I error rate is close to a fixed significance level), and (3) the new parameterization with four parameters and the $c$‐optimal design does not reduce statistical power much while preserving the Type I error rate at a fixed significance level.     

Guidelines for Use of the Approximate Beta‐Poisson Dose–Response Model

For dose–response analysis in quantitative microbial risk assessment (QMRA), the exact beta‐Poisson model is a two‐parameter mechanistic dose–response model with parameters and , which involves the Kummer confluent hypergeometric function. Evaluation of a hypergeometric function is a computational challenge. Denoting as the probability of infection at a given mean dose d, the widely used dose–response model is an approximate formula for the exact beta‐Poisson model. Notwithstanding the required conditions and , issues related to the validity and approximation accuracy of this approximate formula have remained largely ignored in practice, partly because these conditions are too general to provide clear guidance. Consequently, this study proposes a probability measure Pr(0 < r < 1 | , ) as a validity measure (r is a random variable that follows a gamma distribution; and are the maximum likelihood estimates of α and β in the approximate model); and the constraint conditions for as a rule of thumb to ensure an accurate approximation (e.g., Pr(0 < r < 1 | , ) >0.99) . This validity measure and rule of thumb were validated by application to all the completed beta‐Poisson models (related to 85 data sets) from the QMRA community portal (QMRA Wiki). The results showed that the higher the probability Pr(0 < r < 1 | , ), the better the approximation. The results further showed that, among the total 85 models examined, 68 models were identified as valid approximate model applications, which all had a near perfect match to the corresponding exact beta‐Poisson model dose–response curve.     

A Generalized QMRA Beta‐Poisson Dose‐Response Model

Quantitative microbial risk assessment (QMRA) is widely accepted for characterizing the microbial risks associated with food, water, and wastewater. Single‐hit dose‐response models are the most commonly used dose‐response models in QMRA. Denoting as the probability of infection at a given mean dose d, a three‐parameter generalized QMRA beta‐Poisson dose‐response model, , is proposed in which the minimum number of organisms required for causing infection, K_min, is not fixed, but a random variable following a geometric distribution with parameter . The single‐hit beta‐Poisson model, , is a special case of the generalized model with K_min = 1 (which implies ). The generalized beta‐Poisson model is based on a conceptual model with greater detail in the dose‐response mechanism. Since a maximum likelihood solution is not easily available, a likelihood‐free approximate Bayesian computation (ABC) algorithm is employed for parameter estimation. By fitting the generalized model to four experimental data sets from the literature, this study reveals that the posterior median estimates produced fall short of meeting the required condition of = 1 for single‐hit assumption. However, three out of four data sets fitted by the generalized models could not achieve an improvement in goodness of fit. These combined results imply that, at least in some cases, a single‐hit assumption for characterizing the dose‐response process may not be appropriate, but that the more complex models may be difficult to support especially if the sample size is small. The three‐parameter generalized model provides a possibility to investigate the mechanism of a dose‐response process in greater detail than is possible under a single‐hit model.     

Sensitivity Analysis for Critical Control Points Determination and Uncertainty Analysis to Link FSO and Process Criteria: Application to Listeria monocytogenes in Soft Cheese Made from Pasteurized Milk

Microbiological food safety is an important economic and health issue in the context of globalization and presents food business operators with new challenges in providing safe foods. The hazard analysis and critical control point approach involve identifying the main steps in food processing and the physical and chemical parameters that have an impact on the safety of foods. In the risk‐based approach, as defined in the Codex Alimentarius, controlling these parameters in such a way that the final products meet a food safety objective (FSO), fixed by the competent authorities, is a big challenge and of great interest to the food business operators. Process risk models, issued from the quantitative microbiological risk assessment framework, provide useful tools in this respect. We propose a methodology, called multivariate factor mapping (MFM), for establishing a link between process parameters and compliance with a FSO. For a stochastic and dynamic process risk model of in soft cheese made from pasteurized milk with many uncertain inputs, multivariate sensitivity analysis and MFM are combined to (i) identify the critical control points (CCPs) for throughout the food chain and (ii) compute the critical limits of the most influential process parameters, located at the CCPs, with regard to the specific process implemented in the model. Due to certain forms of interaction among parameters, the results show some new possibilities for the management of microbiological hazards when a FSO is specified.     

A Stable Approach Based on Asymptotic Space Integration for Moment‐Independent Uncertainty Importance Measure

The error estimate of Borgonovo's moment‐independent index is considered, and it shows that the possible computational complexity of is mainly due to the probability density function (PDF) estimate because the PDF estimate is an ill‐posed problem and its convergence rate is quite slow. So it reminds us to compute Borgonovo's index using other methods. To avoid the PDF estimate, , which is based on the PDF, is first approximatively represented by the cumulative distribution function (CDF). The CDF estimate is well posed and its convergence rate is always faster than that of the PDF estimate. From the representation, a stable approach is proposed to compute with an adaptive procedure. Since the small probability multidimensional integral needs to be computed in this procedure, a computational strategy named asymptotic space integration is introduced to reduce a high‐dimensional integral to a one‐dimensional integral. Then we can compute the small probability multidimensional integral by adaptive numerical integration in one dimension with an improved convergence rate. From the comparison of numerical error analysis of some examples, it can be shown that the proposed method is an effective approach to uncertainty importance measure computation.     

Fighting the Curse of Sparsity: Probabilistic Sensitivity Measures From Cumulative Distribution Functions

Quantitative models support investigators in several risk analysis applications. The calculation of sensitivity measures is an integral part of this analysis. However, it becomes a computationally challenging task, especially when the number of model inputs is large and the model output is spread over orders of magnitude. We introduce and test a new method for the estimation of global sensitivity measures. The new method relies on the intuition of exploiting the empirical cumulative distribution function of the simulator output. This choice allows the estimators of global sensitivity measures to be based on numbers between 0 and 1, thus fighting the curse of sparsity. For density-based sensitivity measures, we devise an approach based on moving averages that bypasses kernel-density estimation. We compare the new method to approaches for calculating popular risk analysis global sensitivity measures as well as to approaches for computing dependence measures gathering increasing interest in the machine learning and statistics literature (the Hilbert–Schmidt independence criterion and distance covariance). The comparison involves also the number of operations needed to obtain the estimates, an aspect often neglected in global sensitivity studies. We let the estimators undergo several tests, first with the wing-weight test case, then with a computationally challenging code with up to $\text{◂,▸}k=30,000$ inputs, and finally with the traditional Level E benchmark code.     

The Influence of Feelings While Driving Regular Cars on the Perception and Acceptance of Self‐Driving Cars

Self‐driving vehicles will affect the future of transportation, but factors that underlie perception and acceptance of self‐driving cars are yet unclear. Research on feelings as information and the affect heuristic has suggested that feelings are an important source of information, especially in situations of complexity and uncertainty. In this study (N = 1,484), we investigated how feelings related to traditional driving affect risk perception, benefit perception, and trust related to self‐driving cars as well as people's acceptance of the technology. Due to limited experiences with and knowledge of self‐driving cars, we expected that feelings related to a similar experience, namely, driving regular cars, would influence judgments of self‐driving cars. Our results support this assumption. While positive feelings of enjoyment predicted higher benefit perception and trust, negative affect predicted higher risk and higher benefit perception of self‐driving cars. Feelings of control were inversely related to risk and benefit perception, which is in line with research on the affect heuristic. Furthermore, negative affect was an important source of information for judgments of use and acceptance. Interest in using a self‐driving car was also predicted by lower risk perception, higher benefit perception, and higher levels of trust in the technology. Although people's individual experiences with advanced vehicle technologies and knowledge were associated with perceptions and acceptance, many simply have never been exposed to the technology and know little about it. In the absence of this experience or knowledge, all that is left is the knowledge, experience, and feelings they have related to regular driving.     

Age and Gender Differences in Perceived Accident Likelihood and Driver Competences

Road traffic accident involvement rates show clear age and gender differences which may in part be accounted for by differences in risk perception and perceptions of driving competence. The present study extends and replicates that of Matthews and Moran (1986). Young (18–30 years) and older (45–60 years) male and female drivers responded to a questionnaire on perceived accident risk and driving competence (judgment and skill) with respect to themselves and four target groups, and also rated a series of videotaped driving sequences with respect to likelihood of accident occurrence and perceived driving competence. Results showed that effects of rater characteristics were generally confined to the questionnaire. Younger males were perceived as most likely to experience an accident and were judged to be lower than other groups in driving competence. Younger groups showed little bias against older groups and vice versa , but gender-related bias was apparent. The findings of Matthews and Moran were generally confirmed. The results are discussed with reference to four main issues: (1) demographic bias effects—which are generally weak; (2) stereotyping on the basis of gender and/or age of driver; (3) group-specific bias; (4) self-appraisal bias.     

Who is a Distracted Driver? Associations between Mobile Phone Use while Driving,Domain‐Specific Risk Taking,and Personality

Mobile phone use while driving (MPUWD) is an increasingly common form of distracted driving. Given its widespread prevalence, it is important for researchers to identify factors that may predict who is more likely to engage in this risky behavior. The current study investigates associations between MPUWD risk behaviors, domain‐specific risk perceptions, and broad personality dimensions. An Italian community sample (n = 804) completed a survey regarding MPUWD risk perceptions and engagement in MPUWD, in addition to the HEXACO‐PI‐R, a broad six‐factor personality inventory (honesty‐humility, emotionality, extraversion, agreeableness, conscientiousness, openness to experience), and the DOSPERT, a six‐factor domain‐specific self‐report risk‐taking measure (health/safety, recreational, social, ethical, gambling, and investment). With respect to domain‐specific risk taking, greater frequency of SMS use while driving most strongly was associated with greater risk taking for the health/safety, gambling, and ethical risk domains. Further, greater honesty‐humility and conscientiousness, two traits related to cognitive control and risk behaviors, and to a lesser extent openness to experience, were associated with less frequent MPUWD, and positively associated with MPUWD risk perceptions. With growing public safety concern surrounding MPUWD, understanding associated personality factors is not only important for identifying psychological mechanisms underlying risk behavior, but also for more effective prevention and intervention programs.     

Risk Assessment of Multistate Progression of Breast Tumor with State‐Dependent Genetic and Environmental Covariates

Few studies have focused on the different roles risk factors play in the multistate temporal natural course of breast cancer. We proposed a three‐state Markov regression model to predict the risk from free of breast cancer (FBC) to the preclinical screen‐detectable phase (PCDP) and from the PCDP to the clinical phase (CP). We searched the initiators and promoters affecting onset and subsequent progression of breast tumor to build up a three‐state temporal natural history model with state‐dependent genetic and environmental covariates. This risk assessment model was applied to a 1 million Taiwanese women cohort. The proposed model was verified by external validation with another independent data set. We identified three kinds of initiators, including the BRCA gene, seven single nucleotides polymorphism, and breast density. ER, Ki‐67, and HER‐2 were found as promoters. Body mass index and age at first pregnancy both played a role. Among women carrying the BRCA gene, the 10‐year predicted risk for the transition from FBC to CP was 25.83%, 20.31%, and 13.84% for the high‐, intermediate‐, and low‐risk group, respectively. The corresponding figures were 1.55%, 1.22%, and 0.76% among noncarriers. The mean sojourn time of staying at the PCDP ranged from 0.82 years for the highest risk group to 6.21 years for the lowest group. The lack of statistical significance for external validation () revealed the adequacy of our proposed model. The three‐state model with state‐dependent covariates of initiators and promoters was proposed for achieving individually tailored screening and also for personalized clinical surveillance of early breast cancer.     

Social Influence,Risk and Benefit Perceptions,and the Acceptability of Risky Energy Technologies: An Explanatory Model of Nuclear Power Versus Shale Gas

Risky energy technologies are often controversial and debates around them are polarized; in such debates public acceptability is key. Research on public acceptability has emphasized the importance of intrapersonal factors but has largely neglected the influence of interpersonal factors. In an online survey (N = 948) with a representative sample of the United Kingdom, we therefore integrate interpersonal factors (i.e., social influence as measured by social networks) with two risky energy technologies that differ in familiarity (nuclear power vs. shale gas) to examine how these factors explain risk and benefit perceptions and public acceptability. Findings show that benefit perceptions are key in explaining acceptability judgments. However, risk perceptions are more important when people are less familiar with the energy technology. Social network factors affect perceived risks and benefits associated with risky energy technology, hereby indirectly helping to form one's acceptability judgment toward the technology. This effect seems to be present regardless of the perceived familiarity with the energy technology. By integrating interpersonal with intrapersonal factors in an explanatory model, we show how the current “risk–benefit acceptability” model used in risk research can be further developed to advance the current understanding of acceptability formation.     

Risk and Benefit Perceptions of Mobile Phone and Base Station Technology in Bangladesh

Research in developed countries showed that many citizens perceive that radio signals transmitted by mobile phones and base stations represent potential health risks. Less research has been conducted in developing countries focused on citizen perceptions of risks and benefits, despite the recent and rapid introduction of mobile communication technologies. This study aims to identify factors that are influential in determining the tradeoffs that Bangladeshi citizens make between risks and benefits in terms of mobile phone technology acceptance and health concerns associated with the technology. Bangladesh was selected as representative of many developing countries inasmuch as terrestrial telephone infrastructure is insubstantial, and mobile phone use has expanded rapidly over the last decade, even among the poor. Issues of importance were identified in a small‐scale qualitative study among Bangladeshi citizens (n = 13), followed by a survey within a sample of Bangladeshi citizens (n = 500). The results demonstrate that, in general, the perceived benefits of mobile phone technology outweigh the risks. The perceived benefits are primarily related to the social and personal advantages of mobile phone use, including the ability to receive emergency news about floods, cyclones, and other natural disasters. Base stations were seen as a symbol of societal advance. The results furthermore suggest that overall risk perceptions are relatively low, in particular health risks, and are primarily driven by perceptions that related to crime and social inconvenience. Perceived health risks are relatively small. These findings show that risk communication and management may be particularly effective when contextual factors of the society where the system is implemented are taken into consideration.     

How a Nuclear Power Plant Accident Influences Acceptance of Nuclear Power: Results of a Longitudinal Study Before and After the Fukushima Disaster

Major nuclear accidents, such as the recent accident in Fukushima, Japan, have been shown to decrease the public's acceptance of nuclear power. However, little is known about how a serious accident affects people's acceptance of nuclear power and the determinants of acceptance. We conducted a longitudinal study (N= 790) in Switzerland: one survey was done five months before and one directly after the accident in Fukushima. We assessed acceptance, perceived risks, perceived benefits, and trust related to nuclear power stations. In our model, we assumed that both benefit and risk perceptions determine acceptance of nuclear power. We further hypothesized that trust influences benefit and risk perceptions and that trust before a disaster relates to trust after a disaster. Results showed that the acceptance and perceptions of nuclear power as well as its trust were more negative after the accident. In our model, perceived benefits and risks determined the acceptance of nuclear power stations both before and after Fukushima. Trust had strong effects on perceived benefits and risks, at both times. People's trust before Fukushima strongly influenced their trust after the accident. In addition, perceived benefits before Fukushima correlated with perceived benefits after the accident. Thus, the nuclear accident did not seem to have changed the relations between the determinants of acceptance. Even after a severe accident, the public may still consider the benefits as relevant, and trust remains important for determining their risk and benefit perceptions. A discussion of the benefits of nuclear power seems most likely to affect the public's acceptance of nuclear power, even after a nuclear accident.