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Spatial interpolation of soil temperature and water content in the land-water interface using artificial intelligence

From National Research Council Canada

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DOIResolve DOI: https://doi.org/10.3390/w15030473
AuthorSearch for: ORCID identifier: https://orcid.org/0000-0001-5022-1993; Search for: 1ORCID identifier: https://orcid.org/0000-0002-0893-652X; Search for: ORCID identifier: https://orcid.org/0000-0001-5381-8189; Search for: 1ORCID identifier: https://orcid.org/0000-0002-0849-4530; Search for: ORCID identifier: https://orcid.org/0000-0003-3103-9752
Affiliation
  1. National Research Council of Canada. Construction
FormatText, Article
Subjectartificial intelligence; deep learning; RBF networks; rail infrastructure; sharp transition; soil temperature; spatial interpolation; water content
Abstract
The distributed measured data in large regions and remote locations, along with a need to estimate climatic data for point sites where no data have been recorded, has encouraged the implementation of spatial interpolation techniques. Recently, the increasing use of artificial intelligence has become a promising alternative to conventional deterministic algorithms for spatial interpolation. The present study aims to evaluate some machine learning-based algorithms against conventional strategies for interpolating soil temperature data from a region in southeast Canada with an area of 1000 km by 550 km. The radial basis function neural networks (RBFN) and the deep learning approach were used to estimate soil temperature along a railroad after the spline deterministic spatial interpolation method failed to interpolate gridded soil temperature data on the desired locations. The spline method showed weaknesses in interpolating soil temperature data in areas with sudden changes. This limitation did not improve even by increasing the spline nonlinearity. Although both radial basis function neural networks and the deep learning approach had successful performances in interpolating soil temperature data even in sharp transition areas, deep learning outperformed the former method with a normalized RMSE of 9.0% against 16.2% and an R-squared of 89.2% against 53.8%. This finding was confirmed in the same investigation on soil water content.
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PublisherMDPI
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  • Water 15, no. 3, 473 (25 January 2023): 1–21.
LanguageEnglish
Peer reviewedYes
Identifierw15030473
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Record identifieref1ad473-a3a5-4bfb-a557-96829e330b8e
Record created2024-07-30
Record modified2024-07-30
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