Hybrid GA/SIMPLS as alternative regression model in dam deformation analysis

Concrete dam deformation prediction model for health monitoring based on extreme learning machine

“…When the data of T j are complete and continuous in the period, the relationship between y T and T j can be presented as where b j ( j = 0,1 … 5) are unknown coefficients; T 1 is the mean air temperature on the date of observation, and T p‐q represents the average air temperatures in the p to q days before the observation date. When the temperature measurements T j are incomplete or not available, the effect of temperature variations can be described as a sinusoidal functions with 1 year period and 6 months period, and the relationship of them can be described by trigonometric functions as where d = 2π g /365, g (1 ≤ g ≤ 365) is the time in days from the observation date to the beginning date of monitoring…”

“…The dataset is built over a period from January 1991 to December 2000, which has totally 150 groups of data. Finally, 141 groups of data were obtained by means of excluding singular values caused by measurement instruments, human errors, and unexpected occurrences during investigation . The data in the first 8 years that contain 117 samples are adopted as training set, and the rest groups are taken as the testing set.…”

“…Xi et al established a new statistical model by using the artificial immune algorithm to solve the data analysis problem of dam crest displacement and predict the future behaviors of the dam. Xu et al proposed a statistically inspired modification of the PLS regression algorithm with the predictor variables selected by the genetic algorithm with partial least squares to cope with the multicollinearity problem of dam regression models. Rankovic proposed an approach based on the adaptive network‐based fuzzy inference system to predict the dam radial displacement and to identify complex nonlinear relationships between the input and output variables.…”

Data-Based Models for the Prediction of Dam Behaviour: A Review and Some Methodological Considerations

“…They defined a methodology to select the best set of predictors which could be useful to update the predictive model in case of missing variables. A similar approach was followed by Xu et al [57], though with a smaller set of potential inputs.…”

The Impact of Energy, Agriculture, Macroeconomic and Human-Induced Indicators on Environmental Pollution from 1971 to 2011

“…The partial least squares regression analysis has been employed in previous studies outside the scope of the study (Ceglar et al 2016;Mehmood et al 2012;Xu et al 2012), and has been labelled as a flexible method for multivariate analysis. Against the backdrop, the study explores the worth of estimating the impact of energy, agriculture, macroeconomic and human-induced indicators on environmental pollution in Ghana with 21 variables using the statistically inspired modification of partial least squares (SIMPLS).…”