Exploring the sample size and replications scenarios effect on spatial prediction of flood, using MARS and MaxEnt methods case study: saliantape catchment, Golestan, Iran
Author:
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Water Science and Technology
Link
https://link.springer.com/content/pdf/10.1007/s11069-021-04860-0.pdf
Reference66 articles.
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3. Arabameri A, Rezaei K, Cerdà A, Conoscenti C, Kalantari Z (2019) A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran. Sci Total Environ 660:443–458
4. Arabameri A et al (2020) Novel ensemble approaches of machine learning techniques in modeling the gully erosion susceptibility. Remote Sens 12(11):1890
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