Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches
Binh Thai Pham(University of Economics Ho Chi Minh City), Dieu Tien Bui(University of South-Eastern Norway), Ataollah Shirzadi(University of Kurdistan), Indra Prakash(Government of Gujarat), Himan Shahabi(University of Kurdistan), Thi-Thu-Trang Tran(Le Quy Don Technical University), Sushant K. Singh
Cited by 317
Related Papers
Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance
|Earth-Science Reviews|2020|1.1k
A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran
|The Science of The Total Environment|2018|760
A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods
|Journal of Hydrology|2019|726
A novel hybrid artificial intelligence approach for flood susceptibility assessment
|Environmental Modelling & Software|2017|637