Zobrazeno 1 - 10 of 14 pro vyhledávání: '"Xuan Quang Truong"'
1
Akademický článek
Application of potential machine learning models in landslide susceptibility assessment: A case study of Van Yen district, Yen Bai province, Vietnam
Autor: Van Anh Tran, Thanh Dong Khuc, Xuan Quang Truong, An Binh Nguyen, Truong Thanh Phi
Publikováno v: Quaternary Science Advances, Vol 14, Iss , Pp 100181- (2024)
Landslides are natural hazards that cause significant damage to both property and human lives. This study employs potential machine learning models such as Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM) to assess landsli
Externí odkaz: https://doaj.org/article/d294e3b264d145f2a72a4ff63f543bad
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2
Akademický článek
Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree
Autor: Xuan Luan Truong, Muneki Mitamura, Yasuyuki Kono, Venkatesh Raghavan, Go Yonezawa, Xuan Quang Truong, Thi Hang Do, Dieu Tien Bui, Saro Lee
Publikováno v: Applied Sciences, Vol 8, Iss 7, p 1046 (2018)
The objective of this research is introduce a new machine learning ensemble approach that is a hybridization of Bagging ensemble (BE) and Logistic Model Trees (LMTree), named as BE-LMtree, for improving the performance of the landslide susceptibility
Externí odkaz: https://doaj.org/article/6a698cbab17f4ff2bd4ddc50642dd583
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8
Development of HUMGEOSTAT: A New Geological Tool for Geostatistical Analysis of Mineral Deposit: A Case Study at Sin Quyen Mine (Northern Vietnam)
Autor: Tuan Anh Nguyen, Venkatesh Raghavan, Chi Cong Nguyen, Xuan Luan Truong, Xuan Quang Truong
Publikováno v: Journal of the Geological Society of India. 93:574-582
This paper presents the results of the design and implementation of a new geological tool, named as HUMGEOSTAT, for advanced geostatistical analysis of mineral deposit, a case study at the Sin Quyen, one of the important mines for the Vietnam mining
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1e8f22052cf1cedd365a1f8f13e706b6
https://doi.org/10.1007/s12594-019-1219-4
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9
Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree
Autor: Yasuyuki Kono, Thi Hang Do, Saro Lee, Xuan Luan Truong, Venkatesh Raghavan, Xuan Quang Truong, Dieu Tien Bui, Go Yonezawa, Muneki Mitamura
Publikováno v: Applied Sciences, Vol 8, Iss 7, p 1046 (2018)
Applied Sciences
Volume 8
Issue 7
The objective of this research is introduce a new machine learning ensemble approach that is a hybridization of Bagging ensemble (BE) and Logistic Model Trees (LMTree), named as BE-LMtree, for improving the performance of the landslide susceptibility
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4327e135f725397729fa7e03379b8e4
http://www.mdpi.com/2076-3417/8/7/1046
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10
Akademický článek
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