In this article, a new approach to tidal harmonic analysis is introduced. This approach incorporates more constituents in the least squares method for a fixed duration of noise-free tidal record and results in a more accurate tidal prediction. Moreover, it is demonstrated that 135 days of hourly data, which is significantly less than 369 days data in the Rayleigh criterion, is sufficient for th...

The objective of this work is to quantify the uncertainty in probability failure estimates resulting from incomplete knowledge distributions for input random variables. We propose a framework that couples widely used Subset simulation (SuS) with Bayesian/information theoretic multi-model inference. process starts data infer model inputs. Often such sets are small. Multi-model inference assess a...

In this paper, we deal with fuzzy random variables for inputs andoutputs in Data Envelopment Analysis (DEA). These variables are considered as fuzzyrandom flat LR numbers with known distribution. The problem is to find a method forconverting the imprecise chance-constrained DEA model into a crisp one. This can bedone by first, defuzzification of imprecise probability by constructing a suitablem...

I many circumstances, evaluations are based on empirical data. However, some observations may be imprecise, meaning that it is not entirely clear what occurred in them. We address the question of how beliefs are formed in these situations. The individual in our model is essentially a “frequentist.” He first makes a subjective judgment about the occurrence of the event for each imprecise observa...

Data uncertainty is common in emerging applications, such as sensor networks, moving object databases, medical and biological fields. Data uncertainty can be caused by various factors including measurements precision limitation. Data uncertainty is inherited in various applications due to different reasons such as outdated sources or imprecise measurement and transmission problems. Classificati...

On account of the existence of uncertainty, DEA occasionally faces the situation of imprecise data, especially when a set of DMUs include missing data, ordinal data, interval data, stochastic data, or fuzzy data. Therefore, how to evaluate the efficiency of a set of DMUs in interval environments is a problem worth studying. In this paper, we discussed the new method for evaluation and ranking i...

Data uncertainty is common in emerging applications, such as sensor networks, moving object databases, medical and biological fields. Data uncertainty can be caused by various factors including measurements precision limitation. Data uncertainty is inherited in various applications due to different reasons such as outdated sources or imprecise measurement and transmission problems. Classificati...