AdaBoost Metalearning Methodology for Modeling the Incipient Dissociation Conditions of Clathrate Hydrates

Abstract

This paper proposes the AdaBoost metalearning methodology to combine the outcomes of tree-based models of classification and the regression tree (CART) algorithm for estimating the equilibrium dissociation temperature of clathrate hydrates. In addition to the AdaBoost-CART models, models based on the adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches were also developed. Training and testing of the models were done utilizing a gathered database of more than 3500 experimental data on incipient dissociation conditions of CO2 and other hydrate systems. With the average absolute relative deviation percent (AARD%) between 0.03 and 0.07, 0.04 and 1.09, and 0.09 and 1.01, which were obtained by the presented AdaBoost-CART, ANFIS, and ANN models, respectively, the targets were reproduced with satisfactory accuracy. However, for all of the studied clathrate hydrate systems, the proposed AdaBoost-CART models provide more reliable results. Indeed, the obtained AARD% values for tree-based models are lower than those of other models.

A comprehensive approach for osteoporosis detection through chest CT analysis and bone turnover markers: harnessing radiomics and deep learning techniques

“…To establish a predictive model, the eleven most common machine learning models were trained using the final selected clinical factors. These machine models include SVM ( 13 ), KNN ( 14 ), RandomForest ( 15 ), ExtraTrees ( 16 ), XGBoost ( 17 ), LightGBM ( 18 ), NaiveBayes ( 19 ), AdaBoost ( 20 ), GradientBoosting ( 21 ), LR ( 22 ), MLP ( 23 ).…”

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

“… 62 ANN with the hyperbolic tangent sigmoid function predicts hydrate phase boundary conditions in the same range as ANFIS (Gaussian MF). 63 However, in the CO 2 gas hydrate system ANFIS performs better than ANN. 64 Mehrizadeh 65 also confirms the performance of ANFIS over ANN.…”

“…This is highly recommended compared to using more variables that have a weak impact on the hydrate formation conditions . ANN with the hyperbolic tangent sigmoid function predicts hydrate phase boundary conditions in the same range as ANFIS (Gaussian MF) . However, in the CO 2 gas hydrate system ANFIS performs better than ANN .…”

“…AdaBoost-CART provides more reliable HDT prediction than ANFIS and ANN. The hydrate phase behavior prediction error of ANFIS and ANN decreases by 5 times when replaced with AdaBoost-CART . GA-LSSVM model is a modification of the LSSVM and has been used for hydrate phase boundary prediction using gas gravity and pressure as inputs .…”

“…The hydrate phase behavior prediction error of ANFIS and ANN decreases by 5 times when replaced with AdaBoost-CART. 63 GA-LSSVM model is a modification of the LSSVM and has been used for hydrate phase boundary prediction using gas gravity and pressure as inputs. 68 Its predictions are relatively good, with an AARD of about 0.5%.…”