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Liu, Chih-Yu, Ku, Cheng-Yu, Wu, Ting-Yuan, Ku, Yun-Cheng (2024) An Advanced Soil Classification Method Employing the Random Forest Technique in Machine Learning. Applied Sciences, 14 (16) 7202 doi:10.3390/app14167202

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Reference TypeJournal (article/letter/editorial)
TitleAn Advanced Soil Classification Method Employing the Random Forest Technique in Machine Learning
JournalApplied Sciences
AuthorsLiu, Chih-YuAuthor
Ku, Cheng-YuAuthor
Wu, Ting-YuanAuthor
Ku, Yun-ChengAuthor
Year2024 (August 16)Volume14
Page(s)7202Issue16
PublisherMDPI AG
DOIdoi:10.3390/app14167202Search in ResearchGate
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Mindat Ref. ID17548461Long-form Identifiermindat:1:5:17548461:6
GUID0
Full ReferenceLiu, Chih-Yu, Ku, Cheng-Yu, Wu, Ting-Yuan, Ku, Yun-Cheng (2024) An Advanced Soil Classification Method Employing the Random Forest Technique in Machine Learning. Applied Sciences, 14 (16) 7202 doi:10.3390/app14167202
Plain TextLiu, Chih-Yu, Ku, Cheng-Yu, Wu, Ting-Yuan, Ku, Yun-Cheng (2024) An Advanced Soil Classification Method Employing the Random Forest Technique in Machine Learning. Applied Sciences, 14 (16) 7202 doi:10.3390/app14167202
In(2024, August) Applied Sciences Vol. 14 (16) MDPI AG

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Ku, Cheng-Yu, Liu, Chih-Yu, Su, Yan, Yang, Luxi, Huang, Wei-Po (2020) Modeling Tide–Induced Groundwater Response in a Coastal Confined Aquifer Using the Spacetime Collocation Approach. Applied Sciences, 10. 439pp. doi:10.3390/app10020439
Ku, Cheng-Yu, Liu, Chih-Yu, Xiao, Jing-En, Yeih, Weichung (2017) Transient Modeling of Flow in Unsaturated Soils Using a Novel Collocation Meshless Method. Water, 9 (12) 954pp. doi:10.3390/w9120954
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