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abdelaziz mezghani
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Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning.
Sci Total Environ. 2020 Jun 10;720:137320. doi: 10.1016/j.scitotenv.2020.137320. Epub 2020 Feb 15.
Sci Total Environ. 2020.
PMID: 32325551
Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan.
Dou J, Yunus AP, Tien Bui D, Merghadi A, Sahana M, Zhu Z, Chen CW, Khosravi K, Yang Y, Pham BT.
Dou J, et al. Among authors: merghadi a.
Sci Total Environ. 2019 Apr 20;662:332-346. doi: 10.1016/j.scitotenv.2019.01.221. Epub 2019 Jan 21.
Sci Total Environ. 2019.
PMID: 30690368
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Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques.
Chang KT, Merghadi A, Yunus AP, Pham BT, Dou J.
Chang KT, et al. Among authors: merghadi a.
Sci Rep. 2019 Aug 23;9(1):12296. doi: 10.1038/s41598-019-48773-2.
Sci Rep. 2019.
PMID: 31444375
Free PMC article.
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