Tree-based Regression Models for Predicting External Wind Pressure of a Building with an Unconventional Configuration

Meddage, D. P. P.; Ekanayake, I.U.; Weerasuriya, Asiri Umenga; Lewangamage, CS

doi:10.1109/mercon52712.2021.9525734

Cited by 10 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Within the expansive landscape of ML methodologies, a plethora of algorithms and techniques exist to address a myriad of problems. The literature abounds with noteworthy contributions, encompassing gradient boosting regression [ 15 ], random forest [ 16 ], AdaBoost [ 17 ], decision tree [ 18 ], linear regression, and radial basis function neural networks, each offering its unique strengths and suitability for diverse applications.…”

Section: Methodsmentioning

confidence: 99%

Airfoil aerodynamic performance prediction using machine learning and surrogate modeling

Teimourian,

Rohacs,

Dimililer

et al. 2024

Heliyon

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

Airfoil aerodynamic performance prediction using machine learning and surrogate modeling

Teimourian,

Rohacs,

Dimililer

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…Extra Trees regressor learns in a similar way to Random Forest. However, Random Forest uses all feature values to produce results, whereas Extra Trees selects a method in which multiple decision trees randomly select features to produce optimal results [44]. erefore, the learning speed of Extra Trees randomly selecting some features is faster than Random Forest using all feature values.…”

Section: Methodsmentioning

confidence: 99%

Electric Kickboard Demand Prediction in Spatiotemporal Dimension Using Clustering-Aided Bagging Regressor

Park

Lee

Byun

2022

Journal of Advanced Transportation

View full text Add to dashboard Cite

Demand for electric kickboards is increasing specifically in tourist-centric regions worldwide. In order to gain a competitive edge and to provide quality service to customers, it is essential to properly deploy rental electric kickboards (e-kickboards) at the time and place customers want. However, it is necessary to study how to divide the region to predict electric mobility demand by region. Therefore, this study is made to more accurately predict future demand based on past regional customers’ electric mobility demand data. We have proposed a novel electric kickboard demand prediction in spatiotemporal dimension using clustering-aided bagging regressor. We have used electric kickboard usage data from a Jeju, South Korea-based company. As a result of the experiment, it was found that the accuracy before using clustering-based bagging regressor and when the region was divided by the clustering method, the performance was improved, and we have achieved a regression score R 2 of 93.42 using our proposed approach. We have compared our proposed approach with other state-of-the-art models, and we have also compared our model with different other combinations of bagging regressors. This study can be helpful for companies to meet the user’s demand for a better quality of service.

show abstract

Model development using hybrid method for prediction of drug release from biomaterial matrix

Alqarni,

Al Harthi,

Alzubaidi

et al. 2024

Chemometrics and Intelligent Laboratory Systems

View full text Add to dashboard Cite

Tree-based Regression Models for Predicting External Wind Pressure of a Building with an Unconventional Configuration

Cited by 10 publications

References 13 publications

Airfoil aerodynamic performance prediction using machine learning and surrogate modeling

Airfoil aerodynamic performance prediction using machine learning and surrogate modeling

Electric Kickboard Demand Prediction in Spatiotemporal Dimension Using Clustering-Aided Bagging Regressor

Model development using hybrid method for prediction of drug release from biomaterial matrix

Contact Info

Product

Resources

About