Propagation analysis and risk assessment of an active complex landslide using a Monte Carlo statistical approach

AbstractStatistical tests of differential susceptibility have become standard in the empirical literature, and are routinely used to adjudicate between alternative developmental hypotheses. However, their performance and limitations have never been systematically investigated. In this paper I employ Monte Carlo simulations to explore the functioning of three commonly used tests proposed by Roisman et al. (2012). Simulations showed that critical tests of differential susceptibility require considerably larger samples than standard power calculations would suggest. The results also showed that existing criteria for differential susceptibility based on the proportion of interaction index (i.e., values between .40 and .60) are especially likely to produce false negatives and highly sensitive to assumptions about interaction symmetry. As an initial response to these problems, I propose a revised test based on a broader window of proportion of interaction index values (between .20 and .80). Additional simulations showed that the revised test outperforms existing tests of differential susceptibility, considerably improving detection with little effect on the rate of false positives. I conclude by noting the limitations of a purely statistical approach to differential susceptibility, and discussing the implications of the present results for the interpretation of published findings and the design of future studies in this area.

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Comparison of Monte Carlo and Fuzzy Math Simulation Methods for Quantitative Microbial Risk Assessment

Journal of Food Protection ◽

10.4315/0362-028x-66.10.1900 ◽

2003 ◽

Vol 66 (10) ◽

pp. 1900-1910 ◽

Cited By ~ 6

Author(s):

VALERIE J. DAVIDSON ◽

JOANNE RYKS

Keyword(s):

Risk Assessment ◽

Monte Carlo ◽

Monte Carlo Simulations ◽

Interval Arithmetic ◽

Food Systems ◽

Probability Distributions ◽

Economic Cost ◽

Fuzzy Mathematics ◽

Quantitative Microbial Risk Assessment ◽

Mean Values

The objective of food safety risk assessment is to quantify levels of risk for consumers as well as to design improved processing, distribution, and preparation systems that reduce exposure to acceptable limits. Monte Carlo simulation tools have been used to deal with the inherent variability in food systems, but these tools require substantial data for estimates of probability distributions. The objective of this study was to evaluate the use of fuzzy values to represent uncertainty. Fuzzy mathematics and Monte Carlo simulations were compared to analyze the propagation of uncertainty through a number of sequential calculations in two different applications: estimation of biological impacts and economic cost in a general framework and survival of Campylobacter jejuni in a sequence of five poultry processing operations. Estimates of the proportion of a population requiring hospitalization were comparable, but using fuzzy values and interval arithmetic resulted in more conservative estimates of mortality and cost, in terms of the intervals of possible values and mean values, compared to Monte Carlo calculations. In the second application, the two approaches predicted the same reduction in mean concentration (−4 log CFU/ml of rinse), but the limits of the final concentration distribution were wider for the fuzzy estimate (−3.3 to 5.6 log CFU/ml of rinse) compared to the probability estimate (−2.2 to 4.3 log CFU/ml of rinse). Interval arithmetic with fuzzy values considered all possible combinations in calculations and maximum membership grade for each possible result. Consequently, fuzzy results fully included distributions estimated by Monte Carlo simulations but extended to broader limits. When limited data defines probability distributions for all inputs, fuzzy mathematics is a more conservative approach for risk assessment than Monte Carlo simulations.

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Fault Risk Assessment of Underwater Vehicle Steering System Based on Virtual Prototyping and Monte Carlo Simulation

Polish Maritime Research ◽

10.1515/pomr-2016-0038 ◽

2016 ◽

Vol 23 (3) ◽

pp. 97-105

Author(s):

Deyu He ◽

Niaoqing Hu ◽

Lei Hu ◽

Ling Chen ◽

YiPing Guo ◽

...

Keyword(s):

Risk Assessment ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Fault Injection ◽

Fault Simulation ◽

Virtual Prototyping ◽

Underwater Vehicle ◽

Steering System ◽

Assessment Method ◽

Risk Indicator

Abstract Assessing the risks of steering system faults in underwater vehicles is a human-machine-environment (HME) systematic safety field that studies faults in the steering system itself, the driver’s human reliability (HR) and various environmental conditions. This paper proposed a fault risk assessment method for an underwater vehicle steering system based on virtual prototyping and Monte Carlo simulation. A virtual steering system prototype was established and validated to rectify a lack of historic fault data. Fault injection and simulation were conducted to acquire fault simulation data. A Monte Carlo simulation was adopted that integrated randomness due to the human operator and environment. Randomness and uncertainty of the human, machine and environment were integrated in the method to obtain a probabilistic risk indicator. To verify the proposed method, a case of stuck rudder fault (SRF) risk assessment was studied. This method may provide a novel solution for fault risk assessment of a vehicle or other general HME system.

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