Search
Search Results
-
An Efficient Experimental Model to Estimate the Performance of the Raise Borer Drilling Machine Using Linear and Nonlinear Regression Approaches in the Azad Dam in Iran
This research evaluates the chief shaft in the Azad Dam hydroelectric power plant in Iran using a raise borer machine (RBM). Core samples were taken...
-
Quantile Regression
The problem with OLS is demonstrated via Engel’s problem. Concept and definitions of quantiles as well as their connection with other statistical...
-
Study of bioaerosol disinfection kinetics and application of nonlinear regression modeling for optimization of TiO2-based photocatalytic disinfection process
Bacteria and viruses are some of the major sources of indoor air pollution. Many strategies are utilized to control indoor biopollutants. Among the...
-
Estimating seepage losses from lined irrigation canals using nonlinear regression and artificial neural network models
The Slide2 model was used to estimate seepage losses from canals after validation considering different canal geometries, lining thicknesses, and...
-
Innovative soft computing techniques including artificial neural network and nonlinear regression models to predict the compressive strength of environmentally friendly concrete incorporating waste glass powder
Since concrete and mortar productions (industry) are the biggest users of natural resources, its sustainability is under threat. The environmental...
-
Salinity analysis based on multivariate nonlinear regression for web‐based visualization of oceanic data
Traditionally, temperature-salinity (T-S) relationship was analysed to indicate the characteristic of water mass, and prediction models based on...
-
Symbolic Regression
The meaning of symbolic regression is illustrated via Kepler’s problem. The concept based on computer algebra is explained. Genetic algorithm to find...
-
Linear and nonlinear regression analysis of phenol and P-nitrophenol adsorption on a hybrid nanocarbon of ACTF: kinetics, isotherm, and thermodynamic modeling
This study aimed to create activated carbon thin film (ACTF) as a hybrid nanocarbon via a simple and efficient method through a single-step mixing...
-
Performance benchmarking on several regression models applied in urban flash flood risk assessment
To evaluate the performances of regression models applied in the urban flash flood risk assessment, the historical urban flash flood occurrences...
-
-
ROTI-based statistical regression models for GNSS precise point positioning errors associated with ionospheric plasma irregularities
Global Navigation Satellite System (GNSS) signals are susceptible to ionospheric plasma irregularities and associated scintillations, causing large...
-
Prediction of Irrigation Water Quality Indices Using Random Committee, Discretization Regression, REPTree, and Additive Regression
This study aims to evaluate the performance of four ensemble machine learning methods, i.e., Random Committee, Discretization Regression, Reduced...
-
Robust Regression
The concept of robust regression is explained. Different techniques, such as maximum likelihood employing Gröbner basis, Danish algorithm with...
-
Optimized simulation of river flow rate using regression-based models
Given the extreme values of rainfall in recent years and the increase in floods, data-driven models must also be optimized to be able to simulate the...
-
-
Revised Empirical Relations Between Earthquake Source and Rupture Parameters by Regression and Machine Learning Algorithms
In this study, we have developed new empirical relations between various source and rupture parameters such as moment magnitude (M), surface rupture...
-
The Sixth Problem of Probabilistic Regression
The difference of errors-in-variables models to standard regression models is explained. Further the formulae for the total least squares estimator...
-
Bayesian Decomposition Modelling: An Interpretable Nonlinear Approach for Mineral Prospectivity Map**
Prospectivity models that quantify spatial associations between predictor variables and mineralization are critical in data-driven mineral...
-
Multiple linear regression and gene expression programming to predict fracture density from conventional well logs of basement metamorphic rocks
Fracture identification and evaluation requires data from various resources, such as image logs, core samples, seismic data, and conventional well...
-
Improving Gram–Schmidt Adaptive Pansharpening Method Using Support Vector Regression and Markov Random Field
This study aimed to propose an improved Gram–Schmidt adaptive (GSA) pansharpening method using the support vector regression (SVR) and Markov random...
