B
Binh Thai Pham
Researcher at Duy Tan University
Publications - 255
Citations - 15324
Binh Thai Pham is an academic researcher from Duy Tan University. The author has contributed to research in topics: Landslide & Support vector machine. The author has an hindex of 54, co-authored 227 publications receiving 8986 citations. Previous affiliations of Binh Thai Pham include Ton Duc Thang University & Hiroshima University.
Papers
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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.
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A novel hybrid artificial intelligence approach for flood susceptibility assessment
Kamran Chapi,Vijay P. Singh,Ataollah Shirzadi,Himan Shahabi,Dieu Tien Bui,Binh Thai Pham,Khabat Khosravi +6 more
TL;DR: Results indicate that the proposed Bagging-LMT model can be used for sustainable management of flood-prone areas and outperformed all state-of-the-art benchmark soft computing models.
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A comparative study of different machine learning methods for landslide susceptibility assessment
TL;DR: Analysis and comparison of the results show that all five landslide models performed well for landslide susceptibility assessment, but it has been observed that the SVM model has the best performance in comparison to other landslide models.
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A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods
Khabat Khosravi,Himan Shahabi,Binh Thai Pham,Jan Adamowski,Ataollah Shirzadi,Biswajeet Pradhan,Biswajeet Pradhan,Jie Dou,Hai-Bang Ly,Gyula Gróf,Huu Loc Ho,Haoyuan Hong,Kamran Chapi,Indra Prakash +13 more
TL;DR: In this article, three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS and SAW) along with two machine learning methods (NBT and NB) were tested for their ability to model flood susceptibility in one of China's most flood-prone areas, the Ningdu Catchment.