D
Dieu Tien Bui
Researcher at Sewanee: The University of the South
Publications - 271
Citations - 23227
Dieu Tien Bui is an academic researcher from Sewanee: The University of the South. The author has contributed to research in topics: Landslide & Support vector machine. The author has an hindex of 70, co-authored 260 publications receiving 14923 citations. Previous affiliations of Dieu Tien Bui include IT University & Ton Duc Thang University.
Papers
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Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree
TL;DR: This study introduces a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods and demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptible mapping.
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A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility
Wei Chen,Xiaoshen Xie,Jiale Wang,Biswajeet Pradhan,Biswajeet Pradhan,Haoyuan Hong,Dieu Tien Bui,Zhao Duan,Jianquan Ma +8 more
TL;DR: In this article, the authors used three state-of-the-art data mining techniques, namely, logistic model tree (LMT), random forest (RF), and classification and regression tree (CART) models, to map landslide susceptibility.
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A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape.
TL;DR: In this paper, the performance of support vector regression (SVR), artificial neural network (ANN), and random forest (RF) models in predicting and mapping organic carbon (SOC) stocks in the Eastern Mau Forest Reserve, Kenya was compared.
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A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran.
Khabat Khosravi,Binh Thai Pham,Kamran Chapi,Ataollah Shirzadi,Himan Shahabi,Inge Revhaug,Indra Prakash,Dieu Tien Bui +7 more
TL;DR: Results show that the ADT model has the highest prediction capability for flash flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively.
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Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS
TL;DR: Analysis of results indicates that landslide models using machine learning ensemble frameworks are promising methods which can be used as alternatives of individual base classifiers for landslide susceptibility assessment of other prone areas.