Pressure drop modelling in sand filters in micro-irrigation using gradient boosted regression trees

Sand Filters ◽

Micro Irrigation ◽

Multi-site solar power forecasting using gradient boosted regression trees

Solar Energy ◽

10.1016/j.solener.2017.04.066 ◽

2017 ◽

Vol 150 ◽

pp. 423-436 ◽

Cited By ~ 87

Author(s):

Caroline Persson ◽

Peder Bacher ◽

Takahiro Shiga ◽

Henrik Madsen

Keyword(s):

Solar Power ◽

Regression Trees ◽

Gradient Boosted Regression Trees ◽

Solar Power Forecasting ◽

Power Forecasting

Prediction of higher heating value of biomass materials based on proximate analysis using gradient boosted regression trees method

Energy Sources Part A Recovery Utilization and Environmental Effects ◽

10.1080/15567036.2019.1630521 ◽

2019 ◽

Vol 43 (6) ◽

pp. 672-681 ◽

Cited By ~ 6

Author(s):

Seyed Hashem Samadi ◽

Barat Ghobadian ◽

Mohsen Nosrati

Keyword(s):

Regression Trees ◽

Proximate Analysis ◽

Heating Value ◽

Biomass Materials ◽

PredPRBA: Prediction of Protein-RNA Binding Affinity Using Gradient Boosted Regression Trees

Frontiers in Genetics ◽

10.3389/fgene.2019.00637 ◽

2019 ◽

Vol 10 ◽

Cited By ~ 3

Author(s):

Lei Deng ◽

Wenyi Yang ◽

Hui Liu

Keyword(s):

Binding Affinity ◽

Rna Binding ◽

Regression Trees ◽

Wind ramp event prediction with parallelized gradient boosted regression trees

2016 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2016.7727900 ◽

2016 ◽

Cited By ~ 3

Author(s):

Saurav Gupta ◽

Nitin Anand Shrivastava ◽

Abbas Khosravi ◽

Bijaya Ketan Panigrahi

Keyword(s):

Regression Trees ◽

Event Prediction ◽

Estimation of real-driving emissions for buses fueled with liquefied natural gas based on gradient boosted regression trees

The Science of The Total Environment ◽

10.1016/j.scitotenv.2019.01.054 ◽

2019 ◽

Vol 660 ◽

pp. 741-750 ◽

Cited By ~ 40

Author(s):

Yingjiu Pan ◽

Shuyan Chen ◽

Fengxiang Qiao ◽

Satish V. Ukkusuri ◽

Kun Tang

Keyword(s):

Natural Gas ◽

Regression Trees ◽

Liquefied Natural Gas ◽

Prediction of clathrate hydrate phase equilibria using gradient boosted regression trees and deep neural networks

The Journal of Chemical Thermodynamics ◽

10.1016/j.jct.2019.03.030 ◽

2019 ◽

Vol 135 ◽

pp. 86-96 ◽

Cited By ~ 9

Author(s):

Yongchen Song ◽

Hang Zhou ◽

Pengfei Wang ◽

Mingjun Yang

Keyword(s):

Neural Networks ◽

Phase Equilibria ◽

Deep Neural Networks ◽

Regression Trees ◽

Clathrate Hydrate ◽

Hydrate Phase ◽

Cyanotoxin level prediction in a reservoir using gradient boosted regression trees: a case study

Environmental Science and Pollution Research ◽

10.1007/s11356-018-2219-4 ◽

2018 ◽

Vol 25 (23) ◽

pp. 22658-22671 ◽

Cited By ~ 4

Author(s):

Paulino José García Nieto ◽

Esperanza García-Gonzalo ◽

Fernando Sánchez Lasheras ◽

José Ramón Alonso Fernández ◽

Cristina Díaz Muñiz ◽

...

Keyword(s):

Regression Trees ◽

PredRSA: a gradient boosted regression trees approach for predicting protein solvent accessibility

BMC Bioinformatics ◽

10.1186/s12859-015-0851-2 ◽

2016 ◽

Vol 17 (S1) ◽

Cited By ~ 23

Author(s):

Chao Fan ◽

Diwei Liu ◽

Rui Huang ◽

Zhigang Chen ◽

Lei Deng

Keyword(s):

Solvent Accessibility ◽

Regression Trees ◽

Modeling algal atypical proliferation in La Barca reservoir using L-SHADE optimized gradient boosted regression trees: a case study

Neural Computing and Applications ◽

10.1007/s00521-020-05523-0 ◽

2021 ◽

Author(s):

Paulino José García-Nieto ◽

Esperanza García-Gonzalo ◽

José Ramón Alonso Fernández ◽

Cristina Díaz Muñiz

Keyword(s):

Regression Trees ◽

Identification of key phenolic compounds responsible for antioxidant activities of free and bound fractions of blackberry varieties extracts by boosted regression trees

Journal of the Science of Food and Agriculture ◽

10.1002/jsfa.11432 ◽

2021 ◽

Author(s):

Er Sheng Gong ◽

Bin Li ◽

Binxu Li ◽

Natalia S. Podio ◽

Hongyu Chen ◽

...

Keyword(s):

Phenolic Compounds ◽

Antioxidant Activities ◽

Regression Trees ◽

Boosted Regression Trees