Natural hazards are significant problems that every year cause important loses around the world. A good prediction of the behavior of the hazards is a crucial issue to fight against them and to minimize the damages. The models that represent these phenomena need several input parameters and in many cases, such parameters are difficult to know or even to estimate in a real scenario. So, a method...

A challenge when interpreting replications is determining whether the results of a replication "successfully" replicate the original study. Looking for consistency between two studies is challenging because individual studies are susceptible to many sources of error that can cause study results to deviate from each other and the population effect in unpredictable directions and magnitudes. In t...

Existing simulation models used in predicting the surface roughness of a workpiece in finish turning are based on an ideal circular cutting tool nose profile. This leads to a single predicted roughness value for a given set of input parameters. In this paper, a simulation approach that considers the random tool nose profile micro-deviations as well as the tool chatter vibration to predict a rou...

In many practical situations, users select between n alternatives a1, . . . , an, and the only information that we have about the utilities vi of these alternatives are bounds vi ≤ vi ≤ vi. In such situations, it is reasonable to assume that the values vi are independent and uniformly distributed on the corresponding intervals [vi, vi]. Under this assumption, we would like to estimate, for each...

Predicting hurricane tracks and intensity are major challenges. Currently, track prediction models are much more accurate than intensity prediction models. The regression-based Statistical Hurricane Intensity Prediction Scheme (SHIPS), first proposed in 1994, is still the dominant model. In this paper we propose a new model called Prediction Intensity Interval model for Hurricanes (PIIH). Diffe...

In this thesis a methodology to construct prediction intervals for a generic black-box point forecast model is presented. The prediction intervals are learned from the forecasts of the black-box model and the actual realizations of the forecasted variable by using quantile regression on the observed prediction error distribution, the distribution of which is not assumed. An independent meta-mod...

Short-term traffic volume prediction models have been extensively studied in the past few decades. However, most of the previous studies only focus on single-value prediction. Considering the uncertain and chaotic nature of the transportation system, an accurate and reliable prediction interval with upper and lower bounds may be better than a single point value for transportation management. In...

In 1997, Darvasi and Soller presented empirical predictions of the confidence interval of quantitative trait loci (QTL) location for dense marker maps in experimental crosses. They showed from simulation results for backcross and F2 populations from inbred lines that the 95% confidence interval was a simple function of sample size and the effect of the QTL. In this study, we derive by theory si...

it is firstly proved that the multi-input-single-output (miso) fuzzy systems based on interval-valued $r$- and $s$-implications can approximate any continuous function defined on a compact set to arbitrary accuracy.  a formula to compute the lower upper bounds on the number  of interval-valued fuzzy sets needed to achieve a pre-specified approximation  accuracy for an arbitrary multivariate con...