Dieu Tien Bui

Research interests

Publications

• Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance

  Earth-Science Reviews · 2020 · https://doi.org/10.1016/j.earscirev.2020.103225

• Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment

  CATENA · 2020 · https://doi.org/10.1016/j.catena.2019.104426

• Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

  Remote Sensing · 2019 · https://doi.org/10.3390/rs11030230

• Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan

  Landslides · 2019 · https://doi.org/10.1007/s10346-019-01286-5

• Prediction of Blast-Induced Ground Vibration in an Open-Pit Mine by a Novel Hybrid Model Based on Clustering and Artificial Neural Network

  Natural Resources Research · 2019 · https://doi.org/10.1007/s11053-019-09470-z

• A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area

  The Science of The Total Environment · 2019 · https://doi.org/10.1016/j.scitotenv.2019.134413

• Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan

  The Science of The Total Environment · 2019 · https://doi.org/10.1016/j.scitotenv.2019.01.221

• Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches

  CATENA · 2018 · https://doi.org/10.1016/j.catena.2018.12.018

• A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran

  The Science of The Total Environment · 2018 · https://doi.org/10.1016/j.scitotenv.2018.01.266

• Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)

  CATENA · 2018 · https://doi.org/10.1016/j.catena.2018.01.005

• A novel hybrid artificial intelligence approach for flood susceptibility assessment

  Environmental Modelling & Software · 2017 · https://doi.org/10.1016/j.envsoft.2017.06.012

• Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS

  CATENA · 2016 · https://doi.org/10.1016/j.catena.2016.09.007

• A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility

  CATENA · 2016 · https://doi.org/10.1016/j.catena.2016.11.032

• A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)

  Environmental Modelling & Software · 2016 · https://doi.org/10.1016/j.envsoft.2016.07.005

• A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area

  Agricultural and Forest Meteorology · 2016 · https://doi.org/10.1016/j.agrformet.2016.11.002

• Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS

  Journal of Hydrology · 2016 · https://doi.org/10.1016/j.jhydrol.2016.06.027

• GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks

  Environmental Earth Sciences · 2016 · https://doi.org/10.1007/s12665-016-5919-4

• 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

  Landslides · 2015 · https://doi.org/10.1007/s10346-015-0557-6

• Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods

  Theoretical and Applied Climatology · 2015 · https://doi.org/10.1007/s00704-015-1702-9

• Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines

  CATENA · 2015 · https://doi.org/10.1016/j.catena.2015.05.019

• 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

  Ecological Indicators · 2015 · https://doi.org/10.1016/j.ecolind.2014.12.028

• Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models

  Mathematical Problems in Engineering · 2012 · https://doi.org/10.1155/2012/974638

• Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): A comparative assessment of the efficacy of evidential belief functions and fuzzy logic models

  CATENA · 2012 · https://doi.org/10.1016/j.catena.2012.04.001

• Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS

  Computers & Geosciences · 2011 · https://doi.org/10.1016/j.cageo.2011.10.031

• Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression

  Natural Hazards · 2011 · https://doi.org/10.1007/s11069-011-9844-2

Current projects

No projects listed.