A - Papers appearing in refereed journals
Pourghasemi, H. R., Sadhasivam, N., Kariminejad, N. and Collins, A. L. 2020. Gully erosion spatial modelling - Role of machine learning algorithms in selection of the best controlling factors and modelling process. Geoscience Frontiers. https://doi.org/10.1016/j.gsf.2020.03.005
Authors | Pourghasemi, H. R., Sadhasivam, N., Kariminejad, N. and Collins, A. L. |
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Abstract | This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least |
Keywords | Machine learning algorithm; Gully erosion; Random forest; Controlling factors; Variable importance |
Year of Publication | 2020 |
Journal | Geoscience Frontiers |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.gsf.2020.03.005 |
Open access | Published as non-open access |
Funder | Biotechnology and Biological Sciences Research Council |
College of Agriculture, Shiraz University | |
Funder project or code | S2N - Soil to Nutrition - Work package 3 (WP3) - Sustainable intensification - optimisation at multiple scales |
97GRC1M271143 | |
Output status | Published |
Publication dates | |
Online | 25 Mar 2020 |
Publication process dates | |
Accepted | 09 Mar 2020 |
Publisher | Elsevier |
ISSN | 1674-9871 |
Permalink - https://repository.rothamsted.ac.uk/item/97y94/gully-erosion-spatial-modelling-role-of-machine-learning-algorithms-in-selection-of-the-best-controlling-factors-and-modelling-process
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