Dieu Tien Bui

2011–2024 年に発表

137
論文数
26,110
被引用数
83
h 指数
131
i10 指数

被引用数

Dieu Tien Bui の年別被引用数2004 年: 被引用 1 件2008 年: 被引用 1 件2011 年: 被引用 1 件2012 年: 被引用 32 件2013 年: 被引用 29 件2014 年: 被引用 34 件2015 年: 被引用 54 件2016 年: 被引用 162 件2017 年: 被引用 180 件2018 年: 被引用 445 件2019 年: 被引用 791 件2020 年: 被引用 1,242 件2021 年: 被引用 909 件2022 年: 被引用 806 件2023 年: 被引用 727 件2024 年: 被引用 662 件2025 年: 被引用 372 件2026 年: 被引用 46 件2005〜2007 年は被引用が無いため表示していません2009〜2010 年は被引用が無いため表示していません

引用元

国・地域

この著者を引用した国・地域の世界地図中国: 引用元論文 1,416 件、この内訳の 22.9%イラン: 引用元論文 430 件、この内訳の 7%アメリカ合衆国: 引用元論文 402 件、この内訳の 6.5%インド: 引用元論文 389 件、この内訳の 6.3%ベトナム: 引用元論文 360 件、この内訳の 5.8%オーストラリア: 引用元論文 242 件、この内訳の 3.9%マレーシア: 引用元論文 197 件、この内訳の 3.2%韓国: 引用元論文 196 件、この内訳の 3.2%サウジアラビア: 引用元論文 147 件、この内訳の 2.4%カナダ: 引用元論文 134 件、この内訳の 2.2%日本: 引用元論文 122 件、この内訳の 2%イギリス: 引用元論文 120 件、この内訳の 1.9%
0%22.9%その他 32.7%

分野

  • Environmental Science53.5%
  • Engineering16%
  • Computer Science14.4%
  • Agricultural and Biological Sciences2.5%
  • Earth and Planetary Sciences2.4%
  • Medicine2.3%
  • その他8.9%

トピック

  • Landslides and related hazards9.2%
  • Flood Risk Assessment and Management7.7%
  • Fire effects on ecosystems3.2%
  • Remote Sensing in Agriculture2.9%
  • Hydrology and Watershed Management Studies2.4%
  • Soil Geostatistics and Mapping2.4%
  • その他72.2%

共著者

全論文

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

    著者: , , , , , , , - Earth-Science Reviews 2020 被引用: 1,130

  2. 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

    著者: , , , - Ecological Indicators 2015 被引用: 813

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

    著者: , , , , , , , , - Landslides 2019 被引用: 519

  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

    著者: , , , , - Landslides 2015 被引用: 1,254

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

    著者: , , , , , , , , , - The Science of The Total Environment 2019 被引用: 600

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

    著者: , , , , - CATENA 2020 被引用: 465

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

    著者: , , , , , , , - CATENA 2016 被引用: 920

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

    著者: , , , , , , , - The Science of The Total Environment 2018 被引用: 761

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

    著者: , , , , , , , - The Science of The Total Environment 2019 被引用: 394

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

    著者: , , , , - Remote Sensing, Remote. Sens. 2019 被引用: 319

  11. A novel hybrid artificial intelligence approach for flood susceptibility assessment

    著者: , , , , , , - Environmental Modelling & Software, Environ. Model. Softw. 2017 被引用: 638

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

    著者: , , , - CATENA 2016 被引用: 631

  13. Deformation forecasting of a hydropower dam by hybridizing a long short-term memory deep learning network with the coronavirus optimization algorithm

    著者: , , , , , - Computer-Aided Civil and Infrastructure Engineering, Comput. Aided Civ. Infrastructure Eng. 2022 被引用: 69

  14. Soil Salinity Mapping Using SAR Sentinel-1 Data and Advanced Machine Learning Algorithms: A Case Study at Ben Tre Province of the Mekong River Delta (Vietnam)

    著者: , , , , , , - Remote Sensing, Remote. Sens. 2019 被引用: 163

  15. A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping

    著者: , , , , , , - CATENA 2019 被引用: 310

  16. Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India

    著者: , , , , , , , - The Science of The Total Environment 2020 被引用: 211

  17. Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides

    著者: , , , , , , , , - Applied Soft Computing, Appl. Soft Comput. 2021 被引用: 87

  18. Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran

    著者: , , , , , , - Journal of Hydrology 2020 被引用: 206

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

    著者: , , , - Mathematical Problems in Engineering 2012 被引用: 573

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

    著者: , , , , , , , , - CATENA 2018 被引用: 505

  21. 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

    著者: , , , , , - Agricultural and Forest Meteorology 2016 被引用: 389

  22. Image Processing-Based Classification of Asphalt Pavement Cracks Using Support Vector Machine Optimized by Artificial Bee Colony

    著者: , , - Journal of Computing in Civil Engineering, J. Comput. Civ. Eng. 2018 被引用: 140

  23. Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)

    著者: , , , , , , - Remote Sensing, Remote. Sens. 2018 被引用: 270

  24. GIS-based spatial prediction of tropical forest fire danger using a new hybrid machine learning method

    著者: , , - Ecological Informatics, Ecol. Informatics 2018 被引用: 106