Funder
Hanoi University of Civil Engineering
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Water Science and Technology
Reference93 articles.
1. Achour Y, Pourghasemi HR (2020) How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geosci Front. https://doi.org/10.1016/j.gsf.2019.10.001
2. Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology. https://doi.org/10.1016/j.geomorph.2018.06.006
3. Adnan MSG, Rahman MS, Ahmed N, Ahmed B, Rabbi MF, Rahman RM (2020) Improving spatial agreement in machine learning-based landslide susceptibility mapping. Remote Sens. https://doi.org/10.3390/rs12203347
4. Ahlheim M, Frör O, Heinke A, Keil A, Nguyen MD, Pham VD, Saint-Macary C, Zeller M (2008) Landslides in mountainous regions of Northern Vietnam: causes, protection strategies and the assessment of economic losses
5. Akgün A, Bulut F (2007) GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environ Geol. https://doi.org/10.1007/s00254-006-0435-6
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