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Ensemble learning for spatial interpolation of soil potassium content based on environmental information

PloS one, 2015-04, Vol.10 (4), p.e0124383-e0124383 [Peer Reviewed Journal]

COPYRIGHT 2015 Public Library of Science ;COPYRIGHT 2015 Public Library of Science ;2015 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2015 Liu et al 2015 Liu et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0124383 ;PMID: 25928138

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  • Title:
    Ensemble learning for spatial interpolation of soil potassium content based on environmental information
  • Author: Liu, Wei ; Du, Peijun ; Wang, Dongchen
  • Ji, Rongrong
  • Subjects: Computer simulation ; Ensemble learning ; Environmental information ; Environmental Monitoring - methods ; Geology ; Interpolation ; Interpolation methods ; Kriging interpolation ; Land use ; Learning ; Potassium ; Potassium - analysis ; Soil - chemistry ; Soil improvement ; Soil properties ; Soil types ; Spatial discrimination learning
  • Is Part Of: PloS one, 2015-04, Vol.10 (4), p.e0124383-e0124383
  • Description: One important method to obtain the continuous surfaces of soil properties from point samples is spatial interpolation. In this paper, we propose a method that combines ensemble learning with ancillary environmental information for improved interpolation of soil properties (hereafter, EL-SP). First, we calculated the trend value for soil potassium contents at the Qinghai Lake region in China based on measured values. Then, based on soil types, geology types, land use types, and slope data, the remaining residual was simulated with the ensemble learning model. Next, the EL-SP method was applied to interpolate soil potassium contents at the study site. To evaluate the utility of the EL-SP method, we compared its performance with other interpolation methods including universal kriging, inverse distance weighting, ordinary kriging, and ordinary kriging combined geographic information. Results show that EL-SP had a lower mean absolute error and root mean square error than the data produced by the other models tested in this paper. Notably, the EL-SP maps can describe more locally detailed information and more accurate spatial patterns for soil potassium content than the other methods because of the combined use of different types of environmental information; these maps are capable of showing abrupt boundary information for soil potassium content. Furthermore, the EL-SP method not only reduces prediction errors, but it also compliments other environmental information, which makes the spatial interpolation of soil potassium content more reasonable and useful.
  • Publisher: United States: Public Library of Science
  • Language: English
  • Identifier: ISSN: 1932-6203
    EISSN: 1932-6203
    DOI: 10.1371/journal.pone.0124383
    PMID: 25928138
  • Source: PLoS OA刊
    Geneva Foundation Free Medical Journals at publisher websites
    MEDLINE
    PubMed Central
    ProQuest Central
    DOAJ Directory of Open Access Journals
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