Evaluation of Inference Adequacy in Cumulative Logistic Regression Models: An Empirical Validation of ISWRidge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models： An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p<0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to check the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height (X1) and potential energy (X2) significantly impact (p<0.0001) the amplitude-based reflected rate; the P-values for the deviance and Pearson are all >0.05 (0.2839, 0.3438, respectively). That is, the goodness-of-fit between ridge height (X1) and potential energy (X2) can further predict parameters under the scenario of the best parsimonious model.Investigation of 6 predictive powers (R2, Max-rescaled R2, Somers'D, Gamma, Tau-a, and c, respectively) indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.     

利用煤体破裂电磁信号进行局部震源定位方法研究

准确预测煤岩破裂震源方位,确定采掘工作面重点灾害区域,可以使煤岩动力灾害防治措施更为有效.根据煤岩变形破裂电磁效应规律和电磁波基础理论,提出了一种利用电磁信号能量来确定局部震源方位的方法,并通过实验室定向接收实验和平煤十矿现场测试验证该方法的有效性.结果表明,电磁信号强度随着煤岩体应力状态变化而变化;两个正交天线接收的电磁信号能量分别与各自接收夹角余弦值的平方成正比,利用此比例关系可以确定震源的方位;电磁信号定位方法与静态指标法预测局部区域危险性具有较好的检验一致性.研究成果为煤岩动力灾害重点灾害区域的判定提供依据.     

Applying Bayesian belief networks to health risk assessment

The health risk of noncarcinogenic substances is usually represented by the hazard quotient (HQ) or target organ-specific hazard index (TOSHI). However, three problems arise from these indicators. Firstly, the HQ overestimates the health risk of noncarcinogenic substances for non-critical organs. Secondly, the TOSHI makes inappropriately the additive assumption for multiple hazardous substances affecting the same organ. Thirdly, uncertainty of the TOSHI undermines the accuracy of risk characterization. To address these issues, this article proposes the use of Bayesian belief networks (BBN) for health risk assessment (HRA) and the procedure involved is developed using the example of road constructions. According to epidemiological studies and using actual hospital attendance records, the BBN-HRA can specifically identify the probabilistic relationship between an air pollutant and each of its induced disease, which can overcome the overestimation of the HQ for non-critical organs. A fusion technique of conditional probabilities in the BBN-HRA is devised to avoid the unrealistic additive assumption. The use of the BBN-HRA is easy even for those without HRA knowledge. The input of pollution concentrations into the model will bring more concrete information on the morbidity and mortality rates of all the related diseases rather than a single score, which can reduce the uncertainty of the TOSHI.     

Incorporating the LCIA concept into fuzzy risk assessment as a tool for environmental impact assessment

Environmental impact assessment (EIA) is a procedural tool for environmental management that identifies, predicts, evaluates and mitigates the environmental impact of development proposals. In the process of EIA, EIA reports, prepared by developers, are expected to delineate the environmental impact, but in practice they usually determine whether the amounts or concentrations of pollutants comply with the relevant standards. Actually, many analytical tools can improve the analysis of environmental impact in EIA reports, such as life cycle assessment (LCA) and environmental risk assessment (ERA). Life cycle impact assessment (LCIA) is one of steps in LCA that takes account of the causal relationships between environmental hazards and damage. Incorporating the concept of LCIA into an ERA as an integrated tool for the preparation of EIA reports extends the focus of the reports from the regulatory compliance of the environmental impact, to determine the significance of the environmental impact. Sometimes, when using integrated tools, it is necessary to consider fuzzy situations, because of a lack of sufficient information; therefore, so ERA should be generalized to a fuzzy risk assessment (FRA). Therefore, this paper proposes the integration of a LCIA and a FRA as an assessment tool for the preparation of EIA reports, whereby the LCIA clearly identifies the causal linkage for hazard–pathway–receptor–damage and then better explain the significance of the impact; furthermore, a FRA copes with fuzzy and probabilistic situations in the assessment of pollution severity and the estimation of exposure probability. Finally, the use of the proposed methodology is demonstrated in a case study of the expansion plan for the world’s largest plastics processing factory.     

A novel strategy to determine the insurance and risk control plan for natural disaster risk management

This study presents an extended framework for the analysis of economic effects of natural disaster risk management. It also attempts to define and evaluate the optimal insurance arrangements. A model, the economic utility constrained-maximization model, is proposed. The purpose of this study is to establish a strategy for determining an insurance and risk control plan in which consideration is given to balancing the economic effects (e.g., decrease in costs due to damage) by disaster mitigation. Furthermore, these values are compared with risk control actions for purposes of prioritization, to provide data to help evaluate the benefit of each risk control action. Disaster insurance policy premiums in contrast are based on actuarial data taken from situations in which risk control measures are not employed. This can make such contracts unfair to responsible enterprise managers who must take risk control measures. This represents an unfair aspect of insurance policies. Enterprise managers should be able to determine the optimum arrangement between natural disaster risk control and insurance given their budget limitations. The optimal strategies aim at the best applicability and balance between risk control and insurance capability for the enterprise manager. Risk control measures can generate several risk control options for enterprise managers. Premium discounts by insurers are given in this model.     

Hazard management and risk design by optimal statistical analysis

Elicitation methods are used in decision making with respect to risk hazards to allow a researcher to infer the subjective utilities of outcomes from the observed preferences of an individual. A questionnaire method is presented, in this study, which takes into account the inevitable distortion of preferences by random errors and minimizes the effect of such errors. Under mild assumptions, the method for eliciting the utilities of many outcomes is a three-stage procedure. First, the questionnaire is utilized to elicit responses from which a subjective score is defined. Second, individual risk factors are discussed. Finally, the regression model presents individual risk preferences given the overall organizational risk culture, risk management policy, risk identification, and risk analysis. This paper addresses how company managers face risk and their tolerance of risk with respect to risk management.     

Risk and uncertainty analysis in the planning stages of a risk decision-making process

Natural disaster risk, a long-time concern in the insurance industry, is increasingly recognized as a present danger in the business strategies of risk control and enterprise management agencies. Floods and earthquakes can cause massive loss of life and infrastructure, resulting in business interruption and heavy casualties. Many of the short-term developmental strategies employed throughout the world have only served to exacerbate the impact of natural disasters. Therefore, this study presents a review of formal methods that are commonly used in risk and uncertainty analysis in planning and concludes with a critical assessment of the advantages and disadvantages of the different priority setting methods. Our focus is a preliminary proposal for developing an efficient probabilistic approach to facilitate design optimization that involves probabilistic constraints.     

Potential hazard analysis from the viewpoint of flow measurement in large open-channel junctions

A robust and reliable risk assessment procedure for hydrological hazards deserves attention. The transport of mixed material in the types of hazard, such as flash floods and debris flows, plays an important role and should be taken into consideration. At present, however, the mixed material transport phenomenon is not systematically and simply included in the procedures for the elaboration of hazard analysis. The consequence is the risk of losing prediction accuracy and of underestimating hazard impacts. The phenomenon of open-channel junction flow such as in irrigation ditches and at wastewater treatment facilities is an important aspect of hydrological engineering. Extensive experimental studies of the flow characteristics have been conducted. In this study, an Acoustic Doppler Velocimeter is used to monitor the flow in large open-channel junctions at the Chihtan purification plant in Taipei. The condition of the experiment is made up of different discharge. The result of the measurement data shows that the mean velocity profile, aspect ratio, measured vertical line location and the ratio of the distance of the vertical line maximum velocity from the bed to water depth. An artificial neural network (ANN) is used to simulate the mean velocity profiles in this study. The results show that the ANN can accurately and reliably simulate the mean velocity profiles. The result is believed to provide hints for the consideration of the effects, induced by mixed material transport, in the elaboration of hazard analysis.     

Potential hazard analysis and risk assessment of debris flow by fuzzy modeling

Taiwan is a mountainous country, so there is an ever present danger of landslide disasters during the rainy seasons or typhoons. This study aims to develop a fuzzy-rule-based risk assessment model for debris flows and to verify the accuracy of risk assessment so as to help related organizations reduce losses caused by debris flows. The database is comprised of information from actual cases of debris flows that occurred in the Hualien area of Taiwan from 2007 to 2008. The established models can assess the likelihood of the occurrence of debris flows using computed indicators, verify modeling errors, and make comparisons between the existing models for practical applications. In the establishment of a fuzzy-based debris flow risk assessment model, possible for accounting it on the basis of far less information regarding a real system and the information can be of an uncertain, fuzzy or inexact character, the influential factors affecting debris flows include the average terrain slope, catchment area, effective catchment area, accumulated rainfall, rainfall intensity, and geological conditions. The results prove that the risk assessment model systems are quite suitable for debris flow risk assessment, with a resultant ratio of success 96?% and a normalized relative error 4.63?%.