2022 - Research.com Rising Star of Science Award
His primary scientific interests are in Landslide, Support vector machine, Receiver operating characteristic, Cartography and Decision tree. His Landslide study incorporates themes from Artificial neural network, Artificial intelligence and Logistic regression. He has included themes like Machine learning and Pattern recognition in his Artificial intelligence study.
His Support vector machine research includes themes of Elevation, Data mining and Hazard. His studies in Cartography integrate themes in fields like Spatial prediction and Natural hazard. His work deals with themes such as C4.5 algorithm, Field, Normalized Difference Vegetation Index and Topographic Wetness Index, which intersect with Decision tree.
Dieu Tien Bui mainly focuses on Landslide, Artificial intelligence, Support vector machine, Machine learning and Artificial neural network. His studies deal with areas such as Cartography, Decision tree, Data mining and Receiver operating characteristic as well as Landslide. Dieu Tien Bui interconnects Spatial prediction and Natural hazard in the investigation of issues within Cartography.
When carried out as part of a general Artificial intelligence research project, his work on Random forest, Ensemble forecasting and Deep learning is frequently linked to work in Alternating decision tree, therefore connecting diverse disciplines of study. His Support vector machine study combines topics in areas such as Logistic regression, Least squares, Normalized Difference Vegetation Index and Topographic Wetness Index. His research integrates issues of Mean squared error and Metaheuristic in his study of Artificial neural network.
His primary areas of investigation include Artificial intelligence, Machine learning, Artificial neural network, Support vector machine and Data mining. Dieu Tien Bui combines subjects such as Flood myth, Flash flood and Key with his study of Artificial intelligence. Many of his research projects under Machine learning are closely connected to Alternating decision tree with Alternating decision tree, tying the diverse disciplines of science together.
His Artificial neural network study combines topics from a wide range of disciplines, such as Mean squared error and Metaheuristic. As a member of one scientific family, Dieu Tien Bui mostly works in the field of Data mining, focusing on Receiver operating characteristic and, on occasion, Gully erosion, Naive Bayes classifier, Variables and Logistic model tree. His research on Landslide focuses in particular on Landslide susceptibility.
Dieu Tien Bui focuses on Artificial intelligence, Data mining, Support vector machine, Machine learning and Mean squared error. His work in the fields of Decision tree model overlaps with other areas such as Multivariate adaptive regression splines. He has included themes like Artificial neural network, Rotation forest and Hazard in his Data mining study.
His studies in Support vector machine integrate themes in fields like Hybrid approach, Stability, Cluster analysis, Flood myth and Receiver operating characteristic. His work on Random forest and Ensemble forecasting as part of general Machine learning study is frequently linked to Alternating decision tree, bridging the gap between disciplines. His study focuses on the intersection of Random forest and fields such as Landslide with connections in the field of Geographic information system, Natural hazard and Algorithm.
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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
Dieu Tien Bui;Dieu Tien Bui;Tran Anh Tuan;Harald Klempe;Biswajeet Pradhan.
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.
Kennedy Were;Dieu Tien Bui;Øystein B. Dick;Bal Ram Singh.
Ecological Indicators (2015)
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.
Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models
Dieu Tien Bui;Biswajeet Pradhan;Owe Lofman;Inge Revhaug.
Mathematical Problems in Engineering (2012)
A comparative study of different machine learning methods for landslide susceptibility assessment
Binh Thai Pham;Biswajeet Pradhan;Dieu Tien Bui;Indra Prakash.
Environmental Modelling and Software (2016)
Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS
Binh Thai Pham;Dieu Tien Bui;Indra Prakash;M.B. Dholakia.
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.
Science of The Total Environment (2018)
Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS
Dieu Tien Bui;Biswajeet Pradhan;Owe Lofman;Inge Revhaug.
Computers & Geosciences (2012)
Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): A comparative assessment of the efficacy of evidential belief functions and fuzzy logic models
Dieu Tien Bui;Dieu Tien Bui;Biswajeet Pradhan;Owe Lofman;Inge Revhaug.
Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines
Haoyuan Hong;Biswajeet Pradhan;Chong Xu;Dieu Tien Bui.
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